{"id":191,"date":"2026-03-24T09:34:46","date_gmt":"2026-03-24T09:34:46","guid":{"rendered":"https:\/\/www.inhosted.ai\/blog\/?p=191"},"modified":"2026-03-24T09:40:29","modified_gmt":"2026-03-24T09:40:29","slug":"nvidia-h100-vs-h200-llm-training-india","status":"publish","type":"post","link":"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/","title":{"rendered":"NVIDIA H100 vs H200: Which GPU is Right for LLM Training in India?"},"content":{"rendered":"<p><em>A complete 2026 comparison of specs, benchmarks, INR pricing, and workload-specific recommendations \u2014 built exclusively for Indian ML teams.<\/em><\/p><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#1_Introduction_The_GPU_Decision_That_Shapes_Your_LLM_Budget\" >1. Introduction: The GPU Decision That Shapes Your LLM Budget<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Why_This_Decision_Matters_More_in_India_Right_Now\" >Why This Decision Matters More in India Right Now<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#What_This_Article_Covers\" >What This Article Covers<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#2_Technical_Overview_Hopper_Architecture_%E2%80%94_What_They_Share_Where_They_Differ\" >2. Technical Overview: Hopper Architecture \u2014 What They Share, Where They Differ<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#What_H100_and_H200_Have_in_Common\" >What H100 and H200 Have in Common<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Where_They_Diverge_The_Memory_Revolution\" >Where They Diverge: The Memory Revolution<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#What_HBM3e_Actually_Means_in_Practice\" >What HBM3e Actually Means in Practice<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#3_Performance_Benchmarks_Real_Numbers_for_LLM_Workloads\" >3. Performance Benchmarks: Real Numbers for LLM Workloads<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#LLM_Inference_Token_Throughput\" >LLM Inference: Token Throughput<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#LLM_Training_Speed\" >LLM Training Speed<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Energy_Efficiency\" >Energy Efficiency<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#4_Which_GPU_for_Which_LLM_Workload_A_Decision_Guide\" >4. Which GPU for Which LLM Workload? A Decision Guide<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#When_H100_is_the_Right_Choice\" >When H100 is the Right Choice<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#When_H200_is_the_Right_Choice\" >When H200 is the Right Choice<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Decision_Matrix_by_Use_Case\" >Decision Matrix by Use Case<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#5_INR_Pricing_and_Cost_Scenarios_for_Indian_Teams\" >5. INR Pricing and Cost Scenarios for Indian Teams<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Hourly_Rate_Comparison_in_Context\" >Hourly Rate Comparison in Context<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Real_Cost_Scenarios_for_Indian_LLM_Projects\" >Real Cost Scenarios for Indian LLM Projects<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#The_ROI_Crossover_Analysis_When_Does_H200_Cost_Less\" >The ROI Crossover Analysis: When Does H200 Cost Less?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#6_The_India_Context_Why_This_GPU_Decision_Is_Different_Here\" >6. The India Context: Why This GPU Decision Is Different Here<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#The_IndiaAI_Mission_and_Sovereign_GPU_Demand\" >The IndiaAI Mission and Sovereign GPU Demand<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#DPDP_Act_Compliance_and_Data_Residency\" >DPDP Act Compliance and Data Residency<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Indic_LLM_Development_Why_H200_Matters_Specifically_for_India\" >Indic LLM Development: Why H200 Matters Specifically for India<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#7_How_to_Get_Started_on_inhostedai_H100_and_H200_in_Practice\" >7. How to Get Started on inhosted.ai: H100 and H200 in Practice<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Launching_Your_First_GPU_Instance\" >Launching Your First GPU Instance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Recommended_Stack_for_LLM_Training_on_inhostedai\" >Recommended Stack for LLM Training on inhosted.ai<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#Pro_Tips_for_Cost_Optimisation\" >Pro Tips for Cost Optimisation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#8_Frequently_Asked_Questions\" >8. Frequently Asked Questions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#9_Conclusion_The_Right_GPU_for_Your_LLM_Project\" >9. Conclusion: The Right GPU for Your LLM Project<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#The_Verdict\" >The Verdict<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.inhosted.ai\/blog\/nvidia-h100-vs-h200-llm-training-india\/#The_Bigger_Picture_Indias_GPU_Moment\" >The Bigger Picture: India&#8217;s GPU Moment<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n\n<h2><span class=\"ez-toc-section\" id=\"1_Introduction_The_GPU_Decision_That_Shapes_Your_LLM_Budget\"><\/span>1. Introduction: The GPU Decision That Shapes Your LLM Budget<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Picture this: your ML team in Bangalore is about to kick off training a 70B-parameter Indic LLM \u2014 a model that will eventually serve millions of users across 22 Indian languages. Two GPUs are on your shortlist: the <a href=\"https:\/\/www.inhosted.ai\/gpu\/nvidia-h100.php\"><strong>NVIDIA H100<\/strong><\/a> at <strong>\u20b9249.40\/hr<\/strong> and the <a href=\"https:\/\/www.inhosted.ai\/gpu\/nvidia-h200.php\"><strong>NVIDIA H200<\/strong><\/a> at <strong>\u20b9300.14\/hr<\/strong>. The H200 costs 20% more per hour \u2014 but could that premium pay for itself through fewer training hours and simpler infrastructure? That is exactly the question this guide answers, with real INR numbers and India-specific context.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Why_This_Decision_Matters_More_in_India_Right_Now\"><\/span>Why This Decision Matters More in India Right Now<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>India&#8217;s AI infrastructure landscape shifted dramatically in 2025\u201326. Three forces are making the H100 vs H200 decision more consequential than ever for Indian teams:<\/p>\n<ul>\n<li><strong>IndiaAI Mission scale-up: <\/strong>38,000+ GPUs deployed as of early 2026, with \u20b910,372 crore added in the 2026\u201327 Union Budget. Demand for GPU compute \u2014 and the ability to choose the right one \u2014 has never been higher.<\/li>\n<li><strong>Sovereign LLMs pushing compute limits: <\/strong>Sarvam AI&#8217;s 105B-parameter model and Krutrim&#8217;s infrastructure push are setting a new baseline for what Indian AI teams need from a GPU \u2014 requirements that simply did not exist two years ago.<\/li>\n<li><strong>Indian GPU cloud is now competitive: <\/strong>AWS charges \u20b9330+ per hour for H100 access. inhosted.ai offers the same GPU at \u20b9249.40\/hr \u2014 24% cheaper, with Indian data residency and GST invoicing included.<\/li>\n<li><strong>DPDP compliance is tying GPU and provider decisions together: <\/strong>India&#8217;s Digital Personal Data Protection Act (DPDP) 2023 is pushing enterprises toward Indian cloud infrastructure, meaning the GPU choice and the provider choice are increasingly made simultaneously.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_This_Article_Covers\"><\/span>What This Article Covers<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Side-by-side technical spec comparison: memory, bandwidth, compute<\/li>\n<li>Real benchmark data: Llama 2 70B and GPT-3 175B token throughput<\/li>\n<li>Workload-specific recommendations by model size and use case<\/li>\n<li>INR pricing table and detailed cost scenarios for Indian teams<\/li>\n<li>A clear decision framework: which GPU for which job<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<table width=\"936\">\n<tbody>\n<tr>\n<td width=\"936\">\n<p style=\"text-align: center; font-size: 25px;\"><a href=\"https:\/\/www.inhosted.ai\/pricing.php\"><strong>See current H100 and H200 pricing on inhosted.ai<\/strong><br \/>\n<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"2_Technical_Overview_Hopper_Architecture_%E2%80%94_What_They_Share_Where_They_Differ\"><\/span>2. Technical Overview: Hopper Architecture \u2014 What They Share, Where They Differ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"What_H100_and_H200_Have_in_Common\"><\/span>What H100 and H200 Have in Common<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Both GPUs are built on NVIDIA&#8217;s Hopper architecture \u2014 so they share the same fundamental engineering DNA. This matters because it means migrating from H100 to H200 requires zero code changes in your training or inference stack.<\/p>\n<ul>\n<li><strong>4th-generation Tensor Cores <\/strong>with Transformer Engine support for FP8, FP16, and BF16 precision<\/li>\n<li><strong>FP8 inference performance: <\/strong>3,958 TFLOPS on both GPUs \u2014 identical raw compute<\/li>\n<li><strong>Multi-Instance GPU (MIG) <\/strong>support for workload partitioning across multiple users or jobs<\/li>\n<li><strong>NVLink <\/strong>for high-bandwidth multi-GPU scaling<\/li>\n<li><strong>Same 700W TDP: <\/strong>no new cooling infrastructure needed when upgrading from H100 to H200<\/li>\n<li><strong>Identical software stack: <\/strong>CUDA, cuDNN, PyTorch, TensorFlow, Hugging Face \u2014 fully compatible<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<table width=\"936\">\n<tbody>\n<tr>\n<td width=\"936\"><strong><em>Upgrading from H100 to H200 is a true drop-in swap \u2014 no re-architecture, no code changes, no new tooling required.<\/em><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Where_They_Diverge_The_Memory_Revolution\"><\/span>Where They Diverge: The Memory Revolution<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The H200&#8217;s upgrade over the H100 is entirely about memory \u2014 not raw compute. This is deliberate NVIDIA engineering: modern LLMs are memory-bound, not compute-bound. Expanding the memory subsystem delivers real-world performance gains precisely where large models bottleneck.<\/p>\n<p>&nbsp;<\/p>\n<table width=\"936\">\n<thead>\n<tr>\n<td width=\"240\"><strong>Specification<\/strong><\/td>\n<td width=\"228\"><strong>NVIDIA H100<\/strong><\/td>\n<td width=\"228\"><strong>NVIDIA H200<\/strong><\/td>\n<td width=\"240\"><strong>Delta \/ Notes<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td width=\"240\"><strong>Architecture<\/strong><\/td>\n<td width=\"228\">Hopper<\/td>\n<td width=\"228\">Hopper (enhanced)<\/td>\n<td width=\"240\">Same generation<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>Memory Type<\/strong><\/td>\n<td width=\"228\">HBM3<\/td>\n<td width=\"228\">HBM3e<\/td>\n<td width=\"240\">Next-gen memory<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>VRAM Capacity<\/strong><\/td>\n<td width=\"228\">80 GB<\/td>\n<td width=\"228\">141 GB<\/td>\n<td width=\"240\">+76% more VRAM<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>Memory Bandwidth<\/strong><\/td>\n<td width=\"228\">3.35 TB\/s<\/td>\n<td width=\"228\">4.8 TB\/s<\/td>\n<td width=\"240\">+43% faster<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>FP8 Tensor Perf.<\/strong><\/td>\n<td width=\"228\">3,958 TFLOPS<\/td>\n<td width=\"228\">3,958 TFLOPS<\/td>\n<td width=\"240\">Identical<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>TDP (SXM)<\/strong><\/td>\n<td width=\"228\">700 W<\/td>\n<td width=\"228\">700 W<\/td>\n<td width=\"240\">Same \u2014 drop-in swap<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>NVLink Bandwidth<\/strong><\/td>\n<td width=\"228\">600\u2013900 GB\/s<\/td>\n<td width=\"228\">900 GB\/s<\/td>\n<td width=\"240\">Higher default<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>Llama 2 70B Inf.<\/strong><\/td>\n<td width=\"228\">21,806 tok\/s<\/td>\n<td width=\"228\">31,712 tok\/s<\/td>\n<td width=\"240\">+45% throughput<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>vCPUs (inhosted.ai)<\/strong><\/td>\n<td width=\"228\">26<\/td>\n<td width=\"228\">30<\/td>\n<td width=\"240\">+4 vCPUs<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>RAM (inhosted.ai)<\/strong><\/td>\n<td width=\"228\">250 GB<\/td>\n<td width=\"228\">375 GB<\/td>\n<td width=\"240\">+50% RAM<\/td>\n<\/tr>\n<tr>\n<td width=\"240\"><strong>Price\/hr (inhosted)<\/strong><\/td>\n<td width=\"228\">\u20b9249.40<\/td>\n<td width=\"228\">\u20b9300.14<\/td>\n<td width=\"240\">+20% premium<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><em><strong>Note:<\/strong> CPUs and RAM are inhosted.ai instance configurations. TFLOPS and bandwidth are NVIDIA official specifications.<\/em><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_HBM3e_Actually_Means_in_Practice\"><\/span>What HBM3e Actually Means in Practice<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>HBM3e is not a branding refresh it has concrete engineering implications for LLM workloads:<\/p>\n<ul>\n<li><strong>141 GB VRAM: <\/strong>a 70B-parameter model in BF16 precision requires ~140GB. The H200 fits it on a single GPU. The H100 requires two GPUs for the same job.<\/li>\n<li><strong>8 TB\/s bandwidth: <\/strong>feeds the Tensor Cores faster during attention computations, directly reducing time-per-token in generation.<\/li>\n<li><strong>Long-context and RAG advantage: <\/strong>higher bandwidth matters most for memory-bound operations \u2014 long context windows, large batch inference, and retrieval pipelines.<\/li>\n<li><strong>Compute-bound caveat: <\/strong>for small models or dense matrix operations, there is no meaningful difference. H100 and H200 share identical Tensor Core TFLOPS.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"3_Performance_Benchmarks_Real_Numbers_for_LLM_Workloads\"><\/span>3. Performance Benchmarks: Real Numbers for LLM Workloads<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"LLM_Inference_Token_Throughput\"><\/span>LLM Inference: Token Throughput<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The most widely cited benchmark for H100 vs H200 is the MLPerf inference suite running Llama 2 70B:<\/p>\n<ul>\n<li><strong>H100 SXM: <\/strong>21,806 tokens per second (Llama 2 70B, ISL 2K, OSL 128)<\/li>\n<li><strong>H200 SXM: <\/strong>31,712 tokens per second &#8211; a 45% improvement over H100<\/li>\n<li><strong>GPT-3 175B on 8-GPU clusters: <\/strong>H200 delivers 40\u201360% higher throughput than H100<\/li>\n<li><strong>Llama 2 13B: <\/strong>H200 runs approximately 40% faster due to HBM3e feeding the attention layers more efficiently<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>What this means for Indian teams running production inference: a single H200 instance can serve the same request volume as 1.45 H100 instances. For a production service running 24\/7, that translates to roughly 30% fewer inference nodes for the same latency SLA.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"LLM_Training_Speed\"><\/span>LLM Training Speed<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Training is more nuanced than inference. The H200&#8217;s training advantage comes from three mechanisms:<\/p>\n<ul>\n<li><strong>Larger VRAM \u2192 bigger batch sizes: <\/strong>more data per forward\/backward pass, reducing the number of gradient accumulation steps needed.<\/li>\n<li><strong>Higher bandwidth \u2192 faster gradient sync: <\/strong>in multi-GPU setups, inter-GPU communication during backward passes benefits from H200&#8217;s superior memory throughput.<\/li>\n<li><strong>Less activation checkpointing: <\/strong>H200&#8217;s larger VRAM reduces the need to trade compute for memory, allowing faster epoch times.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>However, for compute-bound training phases \u2014 dense matrix multiplications on small models, or small-batch runs \u2014 H100 and H200 perform nearly identically. The advantage compounds specifically on memory-bound phases: attention computation, embedding lookups, and KV-cache management.<\/p>\n<p>&nbsp;<\/p>\n<table width=\"936\">\n<tbody>\n<tr>\n<td width=\"936\"><strong>\u00a0 <em>Practical rule: if your training run is bottlenecked by GPU utilisation &gt;90%, H200 gives marginal benefit. If it&#8217;s bottlenecked by OOM errors, KV-cache thrashing, or model sharding overhead \u2014 H200 can dramatically reduce wall-clock training time.<\/em><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Energy_Efficiency\"><\/span>Energy Efficiency<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Both GPUs operate at the same 700W TDP. This means the H200 delivers its 45% inference performance gain at zero additional power cost \u2014 effectively cutting energy cost per inference token by roughly 31% compared to H100. For Indian data centres operating under power constraints \u2014 a real concern as AI workloads scale \u2014 this is a compounding operational advantage.<\/p>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"4_Which_GPU_for_Which_LLM_Workload_A_Decision_Guide\"><\/span>4. Which GPU for Which LLM Workload? A Decision Guide<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is the question most Indian ML teams are actually asking. Here is a direct, workload-by-workload breakdown.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"When_H100_is_the_Right_Choice\"><\/span>When H100 is the Right Choice<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>Fine-tuning small to medium LLMs (7B\u201330B parameters) <\/strong>using LoRA or QLoRA \u2014 80GB VRAM is more than enough, and H100 at \u20b9249.40\/hr saves 20% vs H200.<\/li>\n<li><strong>Multi-GPU distributed training <\/strong>where you are already scaling horizontally \u2014 NVLink on H100 clusters handles gradient synchronisation efficiently.<\/li>\n<li><strong>Budget-constrained rapid experimentation <\/strong>\u2014 start on H100, migrate to H200 for production once your model architecture is stable.<\/li>\n<li><strong>HPC and scientific workloads <\/strong>that are compute-bound rather than memory-bound \u2014 both GPUs deliver identical TFLOPS.<\/li>\n<li><strong>Stable, existing H100 pipelines <\/strong>\u2014 no reason to change GPU configuration mid-project if your workload fits.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"When_H200_is_the_Right_Choice\"><\/span>When H200 is the Right Choice<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>Training or fine-tuning 70B+ parameter models on a single GPU <\/strong>\u2014 requires &gt;80GB VRAM, which only H200 provides natively.<\/li>\n<li><strong>Long-context LLMs with 128K\u20131M token context windows <\/strong>\u2014 KV-cache grows proportionally with context length and quickly exhausts H100&#8217;s 80GB.<\/li>\n<li><strong>Production inference serving <\/strong>where token throughput directly impacts your latency SLAs and cost-per-token economics.<\/li>\n<li><strong>RAG pipelines with large vector embeddings <\/strong>\u2014 memory bandwidth governs retrieval speed at scale.<\/li>\n<li><strong>Indic LLM development (Sarvam-class, 105B+ models) <\/strong>\u2014 where model sharding across multiple H100s adds engineering complexity and cost.<\/li>\n<li><strong>Multi-modal models <\/strong>combining LLM and vision weights \u2014 combined model size frequently exceeds 80GB.<\/li>\n<li><strong>Agentic AI systems <\/strong>running multiple tools and reasoning loops simultaneously \u2014 memory headroom matters for parallel execution.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Decision_Matrix_by_Use_Case\"><\/span>Decision Matrix by Use Case<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<table width=\"936\">\n<thead>\n<tr>\n<td width=\"220\"><strong>Use Case<\/strong><\/td>\n<td width=\"176\"><strong>Model Size<\/strong><\/td>\n<td width=\"180\"><strong>Recommended<\/strong><\/td>\n<td width=\"360\"><strong>Why<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td width=\"220\">Quick fine-tuning \/ LoRA<\/td>\n<td width=\"176\">7B\u201313B params<\/td>\n<td width=\"180\"><strong>H100 \u2713<\/strong><\/td>\n<td width=\"360\">80GB VRAM is sufficient; save 20% cost<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Full fine-tune, medium LLM<\/td>\n<td width=\"176\">30B\u201370B params<\/td>\n<td width=\"180\"><strong>H100 \u2713<\/strong><\/td>\n<td width=\"360\">Multi-GPU with NVLink covers the memory<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Single-GPU large model<\/td>\n<td width=\"176\">70B+ params<\/td>\n<td width=\"180\"><strong>H200 \u2713<\/strong><\/td>\n<td width=\"360\">141GB VRAM avoids multi-GPU complexity<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Long-context inference (128K+)<\/td>\n<td width=\"176\">Any size<\/td>\n<td width=\"180\"><strong>H200 \u2713<\/strong><\/td>\n<td width=\"360\">HBM3e handles context window memory spikes<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">RAG \/ retrieval inference<\/td>\n<td width=\"176\">Any size<\/td>\n<td width=\"180\"><strong>H200 \u2713<\/strong><\/td>\n<td width=\"360\">Memory bandwidth reduces retrieval latency<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Indic LLM training (105B+)<\/td>\n<td width=\"176\">100B+ params<\/td>\n<td width=\"180\"><strong>H200 \u2713<\/strong><\/td>\n<td width=\"360\">Sarvam-class models need &gt;80GB VRAM<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Production inference serving<\/td>\n<td width=\"176\">7B\u201370B<\/td>\n<td width=\"180\"><strong>H200 \u2713<\/strong><\/td>\n<td width=\"360\">45% faster inference = lower latency SLAs<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Budget dev \/ experimentation<\/td>\n<td width=\"176\">Up to 13B<\/td>\n<td width=\"180\"><strong>H100 \u2713<\/strong><\/td>\n<td width=\"360\">\u20b9249\/hr vs \u20b9300\/hr; same architecture<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Agentic AI \/ multi-modal<\/td>\n<td width=\"176\">Large + vision<\/td>\n<td width=\"180\"><strong>H200 \u2713<\/strong><\/td>\n<td width=\"360\">Memory for combined LLM + vision weights<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong><em>This matrix is the fastest way to make the H100 vs H200 call for any specific project. When in doubt, start with H100 and watch your VRAM utilisation &#8211; if it consistently exceeds 60GB, H200 is your next step.<\/em><\/strong><\/p>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"5_INR_Pricing_and_Cost_Scenarios_for_Indian_Teams\"><\/span>5. INR Pricing and Cost Scenarios for Indian Teams<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This section is the unique value-add that no global GPU cloud blog can replicate. All costs below use inhosted.ai&#8217;s published pricing: H100 at \u20b9249.40\/hr and H200 at \u20b9300.14\/hr.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Hourly_Rate_Comparison_in_Context\"><\/span>Hourly Rate Comparison in Context<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>ai H100: <\/strong>\u20b9249.40\/hr |\u00a0 26 vCPUs\u00a0 |\u00a0 80GB VRAM\u00a0 |\u00a0 250GB RAM<\/li>\n<li><strong>ai H200: <\/strong>\u20b9300.14\/hr |\u00a0 30 vCPUs\u00a0 |\u00a0 141GB VRAM\u00a0 |\u00a0 375GB RAM<\/li>\n<li><strong>AWS India (H100 via P5 instances): <\/strong>~\u20b9330\/hr \u2014 inhosted.ai H100 is 24% cheaper<\/li>\n<li><strong>Azure India (comparable compute): <\/strong>~\u20b9590\/hr \u2014 inhosted.ai H200 is 49% cheaper<\/li>\n<li><strong>IndiaAI Mission subsidised access: <\/strong>\u20b965\/hr for approved projects \u2014 but approval takes weeks. Commercial cloud is the faster path for most startups.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Real_Cost_Scenarios_for_Indian_LLM_Projects\"><\/span>Real Cost Scenarios for Indian LLM Projects<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<table width=\"936\">\n<thead>\n<tr>\n<td width=\"220\"><strong>Scenario<\/strong><\/td>\n<td width=\"90\"><strong>GPU<\/strong><\/td>\n<td width=\"90\"><strong>GPUs<\/strong><\/td>\n<td width=\"136\"><strong>Est. Hours<\/strong><\/td>\n<td width=\"220\"><strong>Total Cost (INR)<\/strong><\/td>\n<td width=\"180\"><strong>Notes<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td width=\"220\">Fine-tune LLaMA 3 8B (LoRA)<\/td>\n<td width=\"90\">H100<\/td>\n<td width=\"90\">1\u00d7<\/td>\n<td width=\"136\">4\u20138 hrs<\/td>\n<td width=\"220\"><strong>\u20b9997 \u2013 \u20b91,995<\/strong><\/td>\n<td width=\"180\">Single run, QLoRA<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Fine-tune Mistral 7B (full)<\/td>\n<td width=\"90\">H100<\/td>\n<td width=\"90\">1\u00d7<\/td>\n<td width=\"136\">20\u201340 hrs<\/td>\n<td width=\"220\"><strong>\u20b94,988 \u2013 \u20b99,976<\/strong><\/td>\n<td width=\"180\">Full fine-tune<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Fine-tune LLaMA 3 70B<\/td>\n<td width=\"90\">H100<\/td>\n<td width=\"90\">4\u00d7<\/td>\n<td width=\"136\">40\u201380 hrs<\/td>\n<td width=\"220\"><strong>\u20b939,904 \u2013 \u20b979,808<\/strong><\/td>\n<td width=\"180\">Multi-GPU setup<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Fine-tune LLaMA 3 70B<\/td>\n<td width=\"90\">H200<\/td>\n<td width=\"90\">2\u00d7<\/td>\n<td width=\"136\">30\u201350 hrs<\/td>\n<td width=\"220\"><strong>\u20b936,017 \u2013 \u20b960,028<\/strong><\/td>\n<td width=\"180\">Fewer GPUs needed<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Train 105B Indic model<\/td>\n<td width=\"90\">H200<\/td>\n<td width=\"90\">8\u00d7<\/td>\n<td width=\"136\">200\u2013400 hrs<\/td>\n<td width=\"220\"><strong>\u20b94,80,224 \u2013 \u20b99,60,448<\/strong><\/td>\n<td width=\"180\">Sarvam-class model<\/td>\n<\/tr>\n<tr>\n<td width=\"220\">Production inference 70B (24\/7)<\/td>\n<td width=\"90\">H200<\/td>\n<td width=\"90\">1\u00d7<\/td>\n<td width=\"136\">720 hrs\/mo<\/td>\n<td width=\"220\"><strong>\u20b92,16,101\/month<\/strong><\/td>\n<td width=\"180\">Single instance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong><em>Estimates based on published benchmark training hours. Actual times vary by model architecture, dataset size, and optimisation techniques (LoRA, gradient checkpointing, mixed precision).<\/em><\/strong><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"The_ROI_Crossover_Analysis_When_Does_H200_Cost_Less\"><\/span>The ROI Crossover Analysis: When Does H200 Cost Less?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The most counterintuitive finding in GPU economics \u2014 and inhosted.ai&#8217;s most compelling sales argument \u2014 is this:<\/p>\n<table width=\"936\">\n<tbody>\n<tr>\n<td width=\"936\"><strong>The H200 is not always more expensive. For 70B+ model workloads:<\/strong><\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2\u00d7 H100 = \u20b9498.80\/hr (required for a 70B model in full BF16 precision)<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1\u00d7 H200 = \u20b9300.14\/hr (same model, single GPU, no NVLink overhead)<\/p>\n<p><strong>Result: H200 is 40% cheaper for this workload \u2014 and simpler to manage.<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li><strong>For inference at scale: <\/strong>H200 produces 45% more tokens\/hr. If your cost is \u20b9X per token, H200 reduces per-token cost by ~31% vs H100 \u2014 the premium pays back immediately.<\/li>\n<li><strong>For models that fit on a single H100: <\/strong>H100 wins on pure cost. The 20% premium buys no meaningful speedup when VRAM is not the bottleneck.<\/li>\n<li><strong>Rule of thumb: <\/strong>if your model exceeds 60GB VRAM usage on H100, the H200 will be faster \u2014 and often cheaper in total spend.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"6_The_India_Context_Why_This_GPU_Decision_Is_Different_Here\"><\/span>6. The India Context: Why This GPU Decision Is Different Here<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"The_IndiaAI_Mission_and_Sovereign_GPU_Demand\"><\/span>The IndiaAI Mission and Sovereign GPU Demand<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The IndiaAI Mission has fundamentally changed India&#8217;s GPU landscape. With 38,000+ NVIDIA GPUs \u2014 including H100 and H200 units \u2014 now deployed at subsidised rates for approved projects, Indian startups and researchers have access to world-class hardware at unprecedented scale. However, the subsidised \u20b965\/hr rate requires government approval, which typically takes several weeks. For most commercial AI startups and enterprises, inhosted.ai&#8217;s on-demand GPU cloud fills the speed gap \u2014 available in under 10 seconds with no approval process.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"DPDP_Act_Compliance_and_Data_Residency\"><\/span>DPDP Act Compliance and Data Residency<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>India&#8217;s Digital Personal Data Protection (DPDP) Act 2023, with implementation progressing through 2025\u201326, creates a clear regulatory rationale for keeping training data and model weights on Indian infrastructure. For teams in healthcare, fintech, and edtech \u2014 industries handling sensitive personal data \u2014 this is not optional. When evaluating H100 vs H200, the provider&#8217;s compliance posture matters as much as the GPU specifications:<\/p>\n<ul>\n<li><strong>ai is <a href=\"https:\/\/www.inhosted.ai\/about-us.php\">ISO 27001<\/a>, ISO 27017, and ISO 27018 certified <\/strong>\u2014 enterprise compliance-ready out of the box.<\/li>\n<li><strong>Data residency: <\/strong>ai operates Tier-III and Tier-IV data centres in India, keeping all compute and data within Indian borders.<\/li>\n<li><strong>GST invoicing: <\/strong>Indian billing infrastructure with full tax compliance \u2014 a practical requirement for Indian enterprises that global providers do not always accommodate easily.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Indic_LLM_Development_Why_H200_Matters_Specifically_for_India\"><\/span>Indic LLM Development: Why H200 Matters Specifically for India<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>India&#8217;s sovereign AI moment is producing models that specifically stress-test GPU memory capacity:<\/p>\n<ul>\n<li><strong>Sarvam AI&#8217;s 105B parameter model <\/strong>(launched February 2026) requires more than H100&#8217;s 80GB VRAM for single-GPU inference \u2014 a concrete, named example of where H200 becomes necessary.<\/li>\n<li><strong>Code-switching workloads: <\/strong>Indic language models handling fluid mixing of Hindi, English, and regional languages require longer effective context windows than comparable English-only models.<\/li>\n<li><strong>Multilingual embedding models: <\/strong>supporting all 22 official Indian languages means storing significantly larger vocabulary embeddings in VRAM \u2014 a memory-intensive requirement that benefits directly from H200&#8217;s 141GB.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"7_How_to_Get_Started_on_inhostedai_H100_and_H200_in_Practice\"><\/span>7. How to Get Started on inhosted.ai: H100 and H200 in Practice<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Launching_Your_First_GPU_Instance\"><\/span>Launching Your First GPU Instance<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li>Visit cloud.inhosted.ai and register \u2014 takes under 5 minutes<\/li>\n<li>Select GPU type: <strong>H100 (\u20b9249.40\/hr, 80GB, 26 vCPUs, 250GB RAM)<\/strong> or <strong>H200 (\u20b9300.14\/hr, 141GB, 30 vCPUs, 375GB RAM)<\/strong><\/li>\n<li>Choose your OS image \u2014 Ubuntu 22.04 recommended for most LLM frameworks<\/li>\n<li>Select storage: at minimum 500GB NVMe SSD for model weights and datasets<\/li>\n<li>Deploy \u2014 average launch time is under 10 seconds<\/li>\n<li>SSH in and run your first training or inference job<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Recommended_Stack_for_LLM_Training_on_inhostedai\"><\/span>Recommended Stack for LLM Training on inhosted.ai<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>Framework: <\/strong>PyTorch 2.x with CUDA 12.x \u2014 pre-installed on inhosted.ai base images<\/li>\n<li><strong>Training library: <\/strong>Hugging Face Transformers + Accelerate for multi-GPU coordination<\/li>\n<li><strong>Efficient fine-tuning: <\/strong>PEFT + LoRA for parameter-efficient fine-tuning on H100<\/li>\n<li><strong>Distributed training: <\/strong>DeepSpeed ZeRO-3 for models over 30B parameters<\/li>\n<li><strong>Experiment tracking: <\/strong>Weights &amp; Biases (W&amp;B) or TensorBoard<\/li>\n<li><strong>Storage: <\/strong>mount <a href=\"https:\/\/10pb.com\/\" target=\"_blank\" rel=\"noopener\"><strong>Object Storage<\/strong><\/a> for dataset access across multiple training runs<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Pro_Tips_for_Cost_Optimisation\"><\/span>Pro Tips for Cost Optimisation<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>Start on H100, move to H200 when needed: <\/strong>prototype and validate your architecture on H100 (\u20b9249.40\/hr), then migrate to H200 only when VRAM usage consistently exceeds 60GB.<\/li>\n<li><strong>Enable gradient checkpointing on H100: <\/strong>reduces VRAM usage by 40\u201350%, extending the effective model size the GPU can handle.<\/li>\n<li><strong>Use BF16 mixed precision training: <\/strong>cuts VRAM requirements roughly in half compared to FP32, with minimal accuracy impact for most LLM workloads.<\/li>\n<li><strong>Calculate per-token economics before choosing: <\/strong>for inference serving, H200&#8217;s 45% throughput improvement often makes it the cheaper option per token delivered.<\/li>\n<li><strong>Contact inhosted.ai sales for committed-use discounts: <\/strong>long-running training jobs (weeks to months) qualify for significant rate reductions.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"8_Frequently_Asked_Questions\"><\/span>8. Frequently Asked Questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>&nbsp;<\/p>\n<p><strong>Q1: Is H200 worth the price premium over H100 for LLM training in India?<\/strong><\/p>\n<p>It depends on your model size. For models under 60B parameters, H100 at \u20b9249.40\/hr is the better value \u2014 the 20% premium buys no meaningful performance improvement when VRAM is not the bottleneck. For 70B+ models, or for production inference serving, the calculus flips. Consider the most direct comparison: 2\u00d7 H100 costs \u20b9498.80\/hr and requires multi-GPU coordination to run a 70B model in full BF16 precision. A single H200 costs \u20b9300.14\/hr, handles the same model on one GPU, and delivers 45% higher inference throughput. For that workload, H200 is <strong>40% cheaper<\/strong> and simpler to manage.<\/p>\n<p><strong>Q2: Can I run LLaMA 3 70B on a single H100 GPU?<\/strong><\/p>\n<p>Not in full BF16 precision \u2014 a 70B model requires approximately 140GB VRAM in that format, which exceeds H100&#8217;s 80GB capacity. However, with 4-bit quantisation (GPTQ or AWQ), a 70B model can run on 40\u201350GB, which fits comfortably within H100&#8217;s 80GB. For full-precision inference or training without quantisation, H200 is the only single-GPU option. If you need full precision and want to use H100, you will need a 2-GPU NVLink setup.<\/p>\n<p><strong>Q3: Which GPU does inhosted.ai recommend for Indic LLM development?<\/strong><\/p>\n<p>H200 \u2014 without qualification for models at 70B parameters and above. India&#8217;s leading sovereign LLMs, including Sarvam AI&#8217;s 105B-parameter model launched in February 2026, consistently require more than H100&#8217;s 80GB VRAM for single-GPU operation. Additionally, Indic language models handling code-switching across 22 official languages, long-context tasks, and multilingual embeddings are inherently memory-intensive workloads that benefit directly from H200&#8217;s 141GB HBM3e memory.<\/p>\n<p><strong>Q4: How does inhosted.ai&#8217;s H100 pricing compare to AWS in India?<\/strong><\/p>\n<p>inhosted.ai offers the H100 at \u20b9249.40\/hr. AWS P5 instances providing H100 access cost approximately \u20b9330\/hr in India \u2014 making inhosted.ai approximately 24% cheaper for comparable GPU compute. Beyond the price difference, inhosted.ai provides Indian data residency, GST-compliant invoicing, ISO 27001\/27017\/27018 certification, and sub-10-second instance launch times \u2014 operational advantages that global hyperscalers do not match on Indian infrastructure.<\/p>\n<p><strong>Q5: Do H100 and H200 use the same software stack?<\/strong><\/p>\n<p>Yes \u2014 fully and completely. Both GPUs are built on NVIDIA&#8217;s Hopper architecture, meaning all CUDA kernels, PyTorch operations, TensorFlow graphs, Hugging Face models, and NVIDIA drivers are 100% compatible across both. Your existing training scripts, fine-tuning pipelines, and inference code will run on H200 without any modifications. This is one of the most practical advantages of the H200: it is a performance upgrade with zero migration cost.<\/p>\n<p><strong>Q6: What is HBM3e and why does it matter for LLMs?<\/strong><\/p>\n<p>HBM3e (High Bandwidth Memory 3e) is the memory technology used in the H200, offering 4.8 TB\/s bandwidth versus H100&#8217;s HBM3 at 3.35 TB\/s \u2014 a 43% improvement. For LLMs specifically, memory bandwidth directly governs how fast attention mechanisms and KV-cache operations execute. These are the primary bottlenecks during autoregressive generation (the process of producing one token at a time). Higher memory bandwidth means more tokens per second \u2014 which is exactly what the benchmark shows: 31,712 tok\/s on H200 versus 21,806 tok\/s on H100 for Llama 2 70B.<\/p>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"9_Conclusion_The_Right_GPU_for_Your_LLM_Project\"><\/span>9. Conclusion: The Right GPU for Your LLM Project<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"The_Verdict\"><\/span>The Verdict<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>&nbsp;<\/p>\n<table width=\"936\">\n<tbody>\n<tr>\n<td width=\"462\"><strong>\u2713\u00a0 Choose H100 if:<\/strong><\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Models are under 60B parameters<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Prototyping or fine-tuning with LoRA\/QLoRA<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Budget optimisation is primary concern<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Scaling horizontally with multi-GPU clusters<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Running stable pipelines \u2014 no config change needed<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Price: \u20b9249.40\/hr on inhosted.ai<\/td>\n<td width=\"462\"><strong>\u2713\u00a0 Choose H200 if:<\/strong><\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Models are 70B+ or require &gt;80GB VRAM<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Maximum inference throughput for production<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Working on Indic LLMs \/ long-context apps<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Avoiding multi-GPU complexity<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Running RAG pipelines or multi-modal models<\/p>\n<p>\u2022\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Price: \u20b9300.14\/hr on inhosted.ai<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"The_Bigger_Picture_Indias_GPU_Moment\"><\/span>The Bigger Picture: India&#8217;s GPU Moment<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The IndiaAI Mission, the emergence of sovereign LLMs like Sarvam AI and Krutrim, and the rapid commercialisation of GPU cloud infrastructure in India are converging into a defining moment for Indian AI development. The GPU you choose today does not just affect this month&#8217;s training bill \u2014 it determines how fast your team can iterate, how quickly you can serve users, and whether your architecture scales cleanly as your models grow.<\/p>\n<p>inhosted.ai exists to make world-class GPU compute accessible to Indian AI teams without the friction of global hyperscalers: no waiting lists, INR billing, Indian data residency, and ISO-certified compliance \u2014 with both H100 and H200 available for launch in <strong>under 10 seconds<\/strong>.<\/p>\n<p>&nbsp;<\/p>\n<table width=\"936\">\n<tbody>\n<tr>\n<td width=\"936\">\n<p style=\"text-align: center; font-size: 25px;\"><strong>Ready to Start Training?<\/strong><\/p>\n<p style=\"text-align: center;\">Launch an H100 or H200 instance on inhosted.ai in under 10 seconds \u2014 no waiting lists, no minimum commitments. Both GPUs available now at transparent INR pricing.<\/p>\n<p style=\"text-align: center;\"><strong>\u00a0 Launch Your GPU \u2192\u00a0 <a href=\"https:\/\/cloud.inhosted.ai\/register\" target=\"_blank\" rel=\"noopener\">cloud.inhosted.ai\/register\u00a0 <\/a><\/strong><\/p>\n<p style=\"text-align: center;\">Compare All GPU Pricing \u2192\u00a0 <a href=\"https:\/\/www.inhosted.ai\/pricing.php\"><strong>inhosted.ai\/pricing.php<\/strong><\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A complete 2026 comparison of specs, benchmarks, INR pricing, and workload-specific recommendations \u2014 built exclusively for Indian ML teams. 1. [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":193,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[13,6,21,22],"tags":[11,24,23,25],"class_list":["post-191","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gpu-server","category-gpu-instances","category-h100","category-h200","tag-gpu-server","tag-h100-gpu","tag-h100-vs-h200","tag-h200-gpu"],"_links":{"self":[{"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/posts\/191","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/comments?post=191"}],"version-history":[{"count":6,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/posts\/191\/revisions"}],"predecessor-version":[{"id":198,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/posts\/191\/revisions\/198"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/media\/193"}],"wp:attachment":[{"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/media?parent=191"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/categories?post=191"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inhosted.ai\/blog\/wp-json\/wp\/v2\/tags?post=191"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}