A complete 2026 comparison of specs, benchmarks, INR pricing, and workload-specific recommendations — built exclusively for Indian ML teams.

1. Introduction: The GPU Decision That Shapes Your LLM Budget

Picture this: your ML team in Bangalore is about to kick off training a 70B-parameter Indic LLM — a model that will eventually serve millions of users across 22 Indian languages. Two GPUs are on your shortlist: the NVIDIA H100 at ₹249.40/hr and the NVIDIA H200 at ₹300.14/hr. The H200 costs 20% more per hour — 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.

Why This Decision Matters More in India Right Now

India’s AI infrastructure landscape shifted dramatically in 2025–26. Three forces are making the H100 vs H200 decision more consequential than ever for Indian teams:

  • IndiaAI Mission scale-up: 38,000+ GPUs deployed as of early 2026, with ₹10,372 crore added in the 2026–27 Union Budget. Demand for GPU compute — and the ability to choose the right one — has never been higher.
  • Sovereign LLMs pushing compute limits: Sarvam AI’s 105B-parameter model and Krutrim’s infrastructure push are setting a new baseline for what Indian AI teams need from a GPU — requirements that simply did not exist two years ago.
  • Indian GPU cloud is now competitive: AWS charges ₹330+ per hour for H100 access. inhosted.ai offers the same GPU at ₹249.40/hr — 24% cheaper, with Indian data residency and GST invoicing included.
  • DPDP compliance is tying GPU and provider decisions together: India’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.

 

What This Article Covers

  • Side-by-side technical spec comparison: memory, bandwidth, compute
  • Real benchmark data: Llama 2 70B and GPT-3 175B token throughput
  • Workload-specific recommendations by model size and use case
  • INR pricing table and detailed cost scenarios for Indian teams
  • A clear decision framework: which GPU for which job

 

See current H100 and H200 pricing on inhosted.ai

 

2. Technical Overview: Hopper Architecture — What They Share, Where They Differ

What H100 and H200 Have in Common

Both GPUs are built on NVIDIA’s Hopper architecture — 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.

  • 4th-generation Tensor Cores with Transformer Engine support for FP8, FP16, and BF16 precision
  • FP8 inference performance: 3,958 TFLOPS on both GPUs — identical raw compute
  • Multi-Instance GPU (MIG) support for workload partitioning across multiple users or jobs
  • NVLink for high-bandwidth multi-GPU scaling
  • Same 700W TDP: no new cooling infrastructure needed when upgrading from H100 to H200
  • Identical software stack: CUDA, cuDNN, PyTorch, TensorFlow, Hugging Face — fully compatible

 

Upgrading from H100 to H200 is a true drop-in swap — no re-architecture, no code changes, no new tooling required.

 

Where They Diverge: The Memory Revolution

The H200’s upgrade over the H100 is entirely about memory — 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.

 

Specification NVIDIA H100 NVIDIA H200 Delta / Notes
Architecture Hopper Hopper (enhanced) Same generation
Memory Type HBM3 HBM3e Next-gen memory
VRAM Capacity 80 GB 141 GB +76% more VRAM
Memory Bandwidth 3.35 TB/s 4.8 TB/s +43% faster
FP8 Tensor Perf. 3,958 TFLOPS 3,958 TFLOPS Identical
TDP (SXM) 700 W 700 W Same — drop-in swap
NVLink Bandwidth 600–900 GB/s 900 GB/s Higher default
Llama 2 70B Inf. 21,806 tok/s 31,712 tok/s +45% throughput
vCPUs (inhosted.ai) 26 30 +4 vCPUs
RAM (inhosted.ai) 250 GB 375 GB +50% RAM
Price/hr (inhosted) ₹249.40 ₹300.14 +20% premium

 

Note: CPUs and RAM are inhosted.ai instance configurations. TFLOPS and bandwidth are NVIDIA official specifications.

 

What HBM3e Actually Means in Practice

HBM3e is not a branding refresh it has concrete engineering implications for LLM workloads:

  • 141 GB VRAM: 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.
  • 8 TB/s bandwidth: feeds the Tensor Cores faster during attention computations, directly reducing time-per-token in generation.
  • Long-context and RAG advantage: higher bandwidth matters most for memory-bound operations — long context windows, large batch inference, and retrieval pipelines.
  • Compute-bound caveat: for small models or dense matrix operations, there is no meaningful difference. H100 and H200 share identical Tensor Core TFLOPS.

 

3. Performance Benchmarks: Real Numbers for LLM Workloads

LLM Inference: Token Throughput

The most widely cited benchmark for H100 vs H200 is the MLPerf inference suite running Llama 2 70B:

  • H100 SXM: 21,806 tokens per second (Llama 2 70B, ISL 2K, OSL 128)
  • H200 SXM: 31,712 tokens per second – a 45% improvement over H100
  • GPT-3 175B on 8-GPU clusters: H200 delivers 40–60% higher throughput than H100
  • Llama 2 13B: H200 runs approximately 40% faster due to HBM3e feeding the attention layers more efficiently

 

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.

 

LLM Training Speed

Training is more nuanced than inference. The H200’s training advantage comes from three mechanisms:

  • Larger VRAM → bigger batch sizes: more data per forward/backward pass, reducing the number of gradient accumulation steps needed.
  • Higher bandwidth → faster gradient sync: in multi-GPU setups, inter-GPU communication during backward passes benefits from H200’s superior memory throughput.
  • Less activation checkpointing: H200’s larger VRAM reduces the need to trade compute for memory, allowing faster epoch times.

 

However, for compute-bound training phases — dense matrix multiplications on small models, or small-batch runs — H100 and H200 perform nearly identically. The advantage compounds specifically on memory-bound phases: attention computation, embedding lookups, and KV-cache management.

 

  Practical rule: if your training run is bottlenecked by GPU utilisation >90%, H200 gives marginal benefit. If it’s bottlenecked by OOM errors, KV-cache thrashing, or model sharding overhead — H200 can dramatically reduce wall-clock training time.

 

Energy Efficiency

Both GPUs operate at the same 700W TDP. This means the H200 delivers its 45% inference performance gain at zero additional power cost — effectively cutting energy cost per inference token by roughly 31% compared to H100. For Indian data centres operating under power constraints — a real concern as AI workloads scale — this is a compounding operational advantage.

 

4. Which GPU for Which LLM Workload? A Decision Guide

This is the question most Indian ML teams are actually asking. Here is a direct, workload-by-workload breakdown.

When H100 is the Right Choice

  • Fine-tuning small to medium LLMs (7B–30B parameters) using LoRA or QLoRA — 80GB VRAM is more than enough, and H100 at ₹249.40/hr saves 20% vs H200.
  • Multi-GPU distributed training where you are already scaling horizontally — NVLink on H100 clusters handles gradient synchronisation efficiently.
  • Budget-constrained rapid experimentation — start on H100, migrate to H200 for production once your model architecture is stable.
  • HPC and scientific workloads that are compute-bound rather than memory-bound — both GPUs deliver identical TFLOPS.
  • Stable, existing H100 pipelines — no reason to change GPU configuration mid-project if your workload fits.

 

When H200 is the Right Choice

  • Training or fine-tuning 70B+ parameter models on a single GPU — requires >80GB VRAM, which only H200 provides natively.
  • Long-context LLMs with 128K–1M token context windows — KV-cache grows proportionally with context length and quickly exhausts H100’s 80GB.
  • Production inference serving where token throughput directly impacts your latency SLAs and cost-per-token economics.
  • RAG pipelines with large vector embeddings — memory bandwidth governs retrieval speed at scale.
  • Indic LLM development (Sarvam-class, 105B+ models) — where model sharding across multiple H100s adds engineering complexity and cost.
  • Multi-modal models combining LLM and vision weights — combined model size frequently exceeds 80GB.
  • Agentic AI systems running multiple tools and reasoning loops simultaneously — memory headroom matters for parallel execution.

 

Decision Matrix by Use Case

Use Case Model Size Recommended Why
Quick fine-tuning / LoRA 7B–13B params H100 ✓ 80GB VRAM is sufficient; save 20% cost
Full fine-tune, medium LLM 30B–70B params H100 ✓ Multi-GPU with NVLink covers the memory
Single-GPU large model 70B+ params H200 ✓ 141GB VRAM avoids multi-GPU complexity
Long-context inference (128K+) Any size H200 ✓ HBM3e handles context window memory spikes
RAG / retrieval inference Any size H200 ✓ Memory bandwidth reduces retrieval latency
Indic LLM training (105B+) 100B+ params H200 ✓ Sarvam-class models need >80GB VRAM
Production inference serving 7B–70B H200 ✓ 45% faster inference = lower latency SLAs
Budget dev / experimentation Up to 13B H100 ✓ ₹249/hr vs ₹300/hr; same architecture
Agentic AI / multi-modal Large + vision H200 ✓ Memory for combined LLM + vision weights

 

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 – if it consistently exceeds 60GB, H200 is your next step.

 

5. INR Pricing and Cost Scenarios for Indian Teams

This section is the unique value-add that no global GPU cloud blog can replicate. All costs below use inhosted.ai’s published pricing: H100 at ₹249.40/hr and H200 at ₹300.14/hr.

Hourly Rate Comparison in Context

  • ai H100: ₹249.40/hr |  26 vCPUs  |  80GB VRAM  |  250GB RAM
  • ai H200: ₹300.14/hr |  30 vCPUs  |  141GB VRAM  |  375GB RAM
  • AWS India (H100 via P5 instances): ~₹330/hr — inhosted.ai H100 is 24% cheaper
  • Azure India (comparable compute): ~₹590/hr — inhosted.ai H200 is 49% cheaper
  • IndiaAI Mission subsidised access: ₹65/hr for approved projects — but approval takes weeks. Commercial cloud is the faster path for most startups.

 

Real Cost Scenarios for Indian LLM Projects

Scenario GPU GPUs Est. Hours Total Cost (INR) Notes
Fine-tune LLaMA 3 8B (LoRA) H100 1× 4–8 hrs ₹997 – ₹1,995 Single run, QLoRA
Fine-tune Mistral 7B (full) H100 1× 20–40 hrs ₹4,988 – ₹9,976 Full fine-tune
Fine-tune LLaMA 3 70B H100 4× 40–80 hrs ₹39,904 – ₹79,808 Multi-GPU setup
Fine-tune LLaMA 3 70B H200 2× 30–50 hrs ₹36,017 – ₹60,028 Fewer GPUs needed
Train 105B Indic model H200 8× 200–400 hrs ₹4,80,224 – ₹9,60,448 Sarvam-class model
Production inference 70B (24/7) H200 1× 720 hrs/mo ₹2,16,101/month Single instance

Estimates based on published benchmark training hours. Actual times vary by model architecture, dataset size, and optimisation techniques (LoRA, gradient checkpointing, mixed precision).

 

The ROI Crossover Analysis: When Does H200 Cost Less?

The most counterintuitive finding in GPU economics — and inhosted.ai’s most compelling sales argument — is this:

The H200 is not always more expensive. For 70B+ model workloads:

•       2× H100 = ₹498.80/hr (required for a 70B model in full BF16 precision)

•       1× H200 = ₹300.14/hr (same model, single GPU, no NVLink overhead)

Result: H200 is 40% cheaper for this workload — and simpler to manage.

 

  • For inference at scale: H200 produces 45% more tokens/hr. If your cost is ₹X per token, H200 reduces per-token cost by ~31% vs H100 — the premium pays back immediately.
  • For models that fit on a single H100: H100 wins on pure cost. The 20% premium buys no meaningful speedup when VRAM is not the bottleneck.
  • Rule of thumb: if your model exceeds 60GB VRAM usage on H100, the H200 will be faster — and often cheaper in total spend.

 

6. The India Context: Why This GPU Decision Is Different Here

The IndiaAI Mission and Sovereign GPU Demand

The IndiaAI Mission has fundamentally changed India’s GPU landscape. With 38,000+ NVIDIA GPUs — including H100 and H200 units — now deployed at subsidised rates for approved projects, Indian startups and researchers have access to world-class hardware at unprecedented scale. However, the subsidised ₹65/hr rate requires government approval, which typically takes several weeks. For most commercial AI startups and enterprises, inhosted.ai’s on-demand GPU cloud fills the speed gap — available in under 10 seconds with no approval process.

DPDP Act Compliance and Data Residency

India’s Digital Personal Data Protection (DPDP) Act 2023, with implementation progressing through 2025–26, creates a clear regulatory rationale for keeping training data and model weights on Indian infrastructure. For teams in healthcare, fintech, and edtech — industries handling sensitive personal data — this is not optional. When evaluating H100 vs H200, the provider’s compliance posture matters as much as the GPU specifications:

  • ai is ISO 27001, ISO 27017, and ISO 27018 certified — enterprise compliance-ready out of the box.
  • Data residency: ai operates Tier-III and Tier-IV data centres in India, keeping all compute and data within Indian borders.
  • GST invoicing: Indian billing infrastructure with full tax compliance — a practical requirement for Indian enterprises that global providers do not always accommodate easily.

 

Indic LLM Development: Why H200 Matters Specifically for India

India’s sovereign AI moment is producing models that specifically stress-test GPU memory capacity:

  • Sarvam AI’s 105B parameter model (launched February 2026) requires more than H100’s 80GB VRAM for single-GPU inference — a concrete, named example of where H200 becomes necessary.
  • Code-switching workloads: Indic language models handling fluid mixing of Hindi, English, and regional languages require longer effective context windows than comparable English-only models.
  • Multilingual embedding models: supporting all 22 official Indian languages means storing significantly larger vocabulary embeddings in VRAM — a memory-intensive requirement that benefits directly from H200’s 141GB.

 

7. How to Get Started on inhosted.ai: H100 and H200 in Practice

Launching Your First GPU Instance

  1. Visit cloud.inhosted.ai and register — takes under 5 minutes
  2. Select GPU type: H100 (₹249.40/hr, 80GB, 26 vCPUs, 250GB RAM) or H200 (₹300.14/hr, 141GB, 30 vCPUs, 375GB RAM)
  3. Choose your OS image — Ubuntu 22.04 recommended for most LLM frameworks
  4. Select storage: at minimum 500GB NVMe SSD for model weights and datasets
  5. Deploy — average launch time is under 10 seconds
  6. SSH in and run your first training or inference job

 

Recommended Stack for LLM Training on inhosted.ai

  • Framework: PyTorch 2.x with CUDA 12.x — pre-installed on inhosted.ai base images
  • Training library: Hugging Face Transformers + Accelerate for multi-GPU coordination
  • Efficient fine-tuning: PEFT + LoRA for parameter-efficient fine-tuning on H100
  • Distributed training: DeepSpeed ZeRO-3 for models over 30B parameters
  • Experiment tracking: Weights & Biases (W&B) or TensorBoard
  • Storage: mount Object Storage for dataset access across multiple training runs

 

Pro Tips for Cost Optimisation

  • Start on H100, move to H200 when needed: prototype and validate your architecture on H100 (₹249.40/hr), then migrate to H200 only when VRAM usage consistently exceeds 60GB.
  • Enable gradient checkpointing on H100: reduces VRAM usage by 40–50%, extending the effective model size the GPU can handle.
  • Use BF16 mixed precision training: cuts VRAM requirements roughly in half compared to FP32, with minimal accuracy impact for most LLM workloads.
  • Calculate per-token economics before choosing: for inference serving, H200’s 45% throughput improvement often makes it the cheaper option per token delivered.
  • Contact inhosted.ai sales for committed-use discounts: long-running training jobs (weeks to months) qualify for significant rate reductions.

 

8. Frequently Asked Questions

 

Q1: Is H200 worth the price premium over H100 for LLM training in India?

It depends on your model size. For models under 60B parameters, H100 at ₹249.40/hr is the better value — 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× H100 costs ₹498.80/hr and requires multi-GPU coordination to run a 70B model in full BF16 precision. A single H200 costs ₹300.14/hr, handles the same model on one GPU, and delivers 45% higher inference throughput. For that workload, H200 is 40% cheaper and simpler to manage.

Q2: Can I run LLaMA 3 70B on a single H100 GPU?

Not in full BF16 precision — a 70B model requires approximately 140GB VRAM in that format, which exceeds H100’s 80GB capacity. However, with 4-bit quantisation (GPTQ or AWQ), a 70B model can run on 40–50GB, which fits comfortably within H100’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.

Q3: Which GPU does inhosted.ai recommend for Indic LLM development?

H200 — without qualification for models at 70B parameters and above. India’s leading sovereign LLMs, including Sarvam AI’s 105B-parameter model launched in February 2026, consistently require more than H100’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’s 141GB HBM3e memory.

Q4: How does inhosted.ai’s H100 pricing compare to AWS in India?

inhosted.ai offers the H100 at ₹249.40/hr. AWS P5 instances providing H100 access cost approximately ₹330/hr in India — 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 — operational advantages that global hyperscalers do not match on Indian infrastructure.

Q5: Do H100 and H200 use the same software stack?

Yes — fully and completely. Both GPUs are built on NVIDIA’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.

Q6: What is HBM3e and why does it matter for LLMs?

HBM3e (High Bandwidth Memory 3e) is the memory technology used in the H200, offering 4.8 TB/s bandwidth versus H100’s HBM3 at 3.35 TB/s — 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 — which is exactly what the benchmark shows: 31,712 tok/s on H200 versus 21,806 tok/s on H100 for Llama 2 70B.

 

9. Conclusion: The Right GPU for Your LLM Project

The Verdict

 

✓  Choose H100 if:

•       Models are under 60B parameters

•       Prototyping or fine-tuning with LoRA/QLoRA

•       Budget optimisation is primary concern

•       Scaling horizontally with multi-GPU clusters

•       Running stable pipelines — no config change needed

•       Price: ₹249.40/hr on inhosted.ai

 ✓  Choose H200 if:

•       Models are 70B+ or require >80GB VRAM

•       Maximum inference throughput for production

•       Working on Indic LLMs / long-context apps

•       Avoiding multi-GPU complexity

•       Running RAG pipelines or multi-modal models

•       Price: ₹300.14/hr on inhosted.ai

 

The Bigger Picture: India’s GPU Moment

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’s training bill — it determines how fast your team can iterate, how quickly you can serve users, and whether your architecture scales cleanly as your models grow.

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 — with both H100 and H200 available for launch in under 10 seconds.

 

Ready to Start Training?

Launch an H100 or H200 instance on inhosted.ai in under 10 seconds — no waiting lists, no minimum commitments. Both GPUs available now at transparent INR pricing.

  Launch Your GPU →  cloud.inhosted.ai/register 

Compare All GPU Pricing →  inhosted.ai/pricing.php