Introducing NVIDIA A100 GPUs unified acceleration of AI, data, and HPC workloads NVIDIA A100 GPUs have the capability to train and fine-tune large language models, execute multimodal AI, and deliver AI-driven data-intensive pipelines at demonstrated enterprise-class stability. A100 is based on the Ampere architecture with high bandwidth HBM2e memory, NVLink and provides outstanding performance on LLMs, recommendation engines, analytics, and large-scale scientific computing.
80 GB HBM2e with ECC
1,248 TFLOPS
Up to 624 TFLOPS
2.0 TB/s
600 GB/s Bidirectional (per GPU, NVLink)
Agility, predictable scale and performance - drag-free DevOps.
Like A100 processes, spin up A100 clusters with LLMs, recommenders, and multi-modal workloads, auto scaling and spot-aware scheduling, and job templates to replicate experiments, slots per minute at auto scaling theory level, etc
IoT Unfined to large batches and streaming ETL. Training curves and high-QPS inference Stable training curves and high-QPS inference are achieved by feeding A100s at line-rate.
Role-based access, GPU quota, audit log, secrets management - everything to make AI applications safe and collaborative to production teams.
Enterprises select Inhosted.ai to deploy A100 cluster that is optimized to achieve consistent throughput, transparent billing, and uptime to scale and be available on secure compliance-ready infrastructure.
Predictably run billion-parameter models, recommendation engines and embeddings. The A100 balancing performance is used to provide consistent iterations, rapid convergence, and reduce experiment cost.
End-to-end pipelines Orchestrate end-to-end pipelines, such as data prep, pretrain, fine-tune, and batch inference, with CUDA, cuDNN, TensorRT, Triton, and the PyData ecosystem. One cluster, many workloads.
Every deployment will be in ISO 27001 certified and SOC-certified facilities with both in-rest and in-transit encryption. Data is secure amongst teams and projects due to network segmentation, per-tenant isolation and hardened images.
Deploy where your users are. Inhosted.ai is provided with multi-region availability, auto failover, constant latency, and 99.95% uptime SLA - so your training and your inference is not interrupted.
The NVIDIA A100 GPu servers provide high performance in AI training, inference and scale computing. They are constructed on high-bandwidth memory and high-end architecture to deliver a reliable performance to high workloads and offer the flexibility of the current data centers. The balance in power, efficiency, and scalability of the nvidia a100 price is advantageous to the organization when it is evaluated.
NVIDIA A100 is an enterprise-focused product, which allows to effectively use resources and reproduce the performance with a workload. To teams looking at the alternatives of nvidia gpu prices, it offers great value in terms of high throughput, reliability, and long-term efficiency.
No middlemen. No shared footprints. End-to-end control of power, cooling, networking and security—so your AI workloads run faster, safer, and more predictably.
The NVIDIA A100 sets the benchmark for versatile, data-center AI — accelerating training, inference, and analytics with outstanding efficiency. Experience faster time-to-accuracy, better memory bandwidth utilization, and elastic scaling across clusters with MIG and NVLink-enabled topologies.
Faster model training vs previous gen on mixed precision
Higher inference throughput with MIG partitioning
High-bandwidth HBM2e for large batch sizes
Uptime on Inhosted.ai GPU cloud
Where the NVIDIA A100 can convert workloads into real-world application, whether it is advanced AI training or a high-performance computing workload, and provides scalable solutions to the team that needs a low-cost cloud server, but with enterprise-level performance.
A100 GPUs are capable of performing exceptionally with regard to the training of large language models, vision systems, and multimodal networks. High throughput and consistent performance also make Teams suitable in cases where organizations are in need of a powerful GPU VPS server to speed up the development of AI.
Massive datasets Process massive data with low latency by taking advantage of GPU-accelerated analytics. A100 allows improving the feature engineering process, real-time monitoring, and predictive insights and is cost-effective relative to the legacy infrastructure.
A100 is used to power high-precision large-scale compute, whether through scientific simulation or engineering workload. It offers good performance-price balance to the businesses that require a dependable cheap cloud server with the demanding workload of HPC.
A100 shortens iteration cycles for translation, summarization, and RAG pipelines — and serves models with predictable latency.
Run video processing, image creation, and creative AI processes with the use of GPU-based pipelines. A100 provides an uninterrupted operation in real-time rendering, analysis, and content creation.
Embeddings and real-time inference Power recommendation engines. A100 also provides a consistent performance, and it is optimal in the case of personalization engines operating under a scalable GPU VPS server environment.
At inhosted.ai, we empower AI-driven businesses with enterprise-grade GPU infrastructure. From GenAI startups to Fortune 500 labs, our customers rely on us for consistent performance, scalability, and round-the-clock reliability. Here's what they say about working with us.
Join Our GPU Cloud
"inhosted.ai helped us move GPU workloads in seconds. Uptime has been rock-solid, and performance consistent across regions — exactly what we needed for live inference."
"Best experience we’ve had with GPU cloud. Instant spin-ups, clear billing, and quick support. Our vision models deploy faster and stay within budget."
"We run multi-region inference and scheduled retraining on inhosted.ai. Scaling from 10 to 400+ GPUs takes minutes, networking is consistent, and storage hits the throughput we need."
"Training times dropped and costs stayed predictable. The support team was proactive throughout deployment."
"Migrating our LLM training stack to inhosted.ai gave us a 3× throughput boost. H100 clusters came online in seconds and billing stayed predictable. We cut project timelines by weeks."
"Predictable pricing, high GPU availability, and fast storage — we ship models faster with fewer surprises."
"Training times dropped and costs stayed predictable. The support team was proactive throughout deployment."
"Migrating our LLM training stack to inhosted.ai gave us a 3× throughput boost. H100 clusters came online in seconds and billing stayed predictable. We cut project timelines by weeks."
"Predictable pricing, high GPU availability, and fast storage — we ship models faster with fewer surprises."
"inhosted.ai helped us move GPU workloads in seconds. Uptime has been rock-solid, and performance consistent across regions — exactly what we needed for live inference."
"Best experience we’ve had with GPU cloud. Instant spin-ups, clear billing, and quick support. Our vision models deploy faster and stay within budget."
"We run multi-region inference and scheduled retraining on inhosted.ai. Scaling from 10 to 400+ GPUs takes minutes, networking is consistent, and storage hits the throughput we need."
A100 is a high-end data-center graphic card, and it costs differently depending on the configuration and the provider. Its enterprise performance and reliability make it generally costly as compared to consumer GPUs.
This is true, the A100 is more suitable to AI training, big models, and workloads in data-centers. RTX 4090 will be more appropriate with game and creative works.
Yes. A100 GPU is much more capable of AI and compute workloads than the RTX 3090, particularly large scale training.
The A100 is an enterprise-level card accelerator, which is meant to be used in AI, deep learning, and high-performance computing.
The RTX 5090 is yet to be officially released. The A100 will still be better when it comes to enterprise AI and data-center loads, even when it is released.
The A100 is more suitable to be used in heavy AI training and HPC, whereas the L4 GPU is more appropriate in the inference and cost-effective workloads.
Yes, when performance using AI needs high training, large data processing, or workloads across the company. Smaller GPUs might be cheaper in case of light work types.
It will technically be yes but not practical. The A100 needs dedicated power, cooling and server grade hardware.
Renting is recommended in case of short term or flexible requirement whereas purchasing is suitable when there is long term, continuous use with own infrastructure.
