Hopper Architecture
Rent NVIDIA H200 Cloud GPU
The upgraded H100 with 1.8x more memory. 141GB HBM3e, ~18,432 CUDA cores, Transformer Engine, and 4,800 GB/s memory bandwidth. The most memory-dense data center GPU ever built for AI training and large model inference. Coming soon on VectorLay.
NVIDIA H200: The Memory-Dense Successor to the H100
The NVIDIA H200 is the memory-optimized evolution of the H100, built on the same Hopper architecture but equipped with 141GB of HBM3e—the fastest and highest-capacity memory ever put on a GPU. Where the H100's 80GB HBM3 set the standard for data center AI, the H200 raises the bar with 1.8x more memory capacity and 4,800 GB/s of bandwidth, a 43% increase over the H100.
This massive memory upgrade directly addresses the biggest constraint in modern AI: model size. Large language models are growing exponentially, and the 80GB ceiling of the H100 increasingly forces teams to split models across multiple GPUs. The H200's 141GB HBM3e allows models like Llama 3.1 70B at full precision to run comfortably on a single GPU with room to spare for large KV caches, enabling higher throughput and lower latency in production inference.
The H200 retains all of the H100's compute capabilities—the Transformer Engine, FP8 support, 4th-gen Tensor Cores, and NVLink connectivity—while adding the memory headroom that today's largest models demand. For teams running memory-bound workloads, the H200 is the most capable GPU available before the Blackwell generation.
H200 Technical Specifications
| Specification | H200 SXM |
|---|---|
| GPU Architecture | Hopper (GH200) |
| VRAM | 141GB HBM3e |
| CUDA Cores | ~18,432 |
| Memory Bandwidth | 4,800 GB/s |
| FP16 (Tensor) | 989 TFLOPS |
| FP8 (Tensor) | 3,958 TFLOPS (with sparsity) |
| TDP | 700W |
| Memory Type | HBM3e (6th Gen) |
| NVLink | 4th Gen, 900 GB/s |
| Transformer Engine | Yes (FP8 auto-mixed precision) |
The H200's defining feature is its 141GB of HBM3e memory—the largest memory pool ever fitted on a single GPU. HBM3e delivers higher bandwidth and lower power consumption than HBM3, achieving 4,800 GB/s of throughput. This is critical for inference workloads where memory bandwidth directly determines tokens-per-second performance. The H200 can serve the same models as the H100 but with significantly higher throughput thanks to the bandwidth increase.
H200 Cloud GPU Pricing Comparison
VectorLay is preparing to offer H200 GPUs at competitive pricing. In the meantime, here's how current cloud providers price the H200:
| Provider | GPU | $/hour | $/month (est.) |
|---|---|---|---|
| VectorLay | H200 | Coming soon | — |
| CoreWeave | H200 (141GB) | ~$3.29 | ~$2,369 |
| Lambda Labs | H200 (141GB) | ~$3.49 | ~$2,513 |
| RunPod | H200 (141GB) | ~$3.89 | ~$2,801 |
| AWS | H200 (141GB) | ~$5.50 (est.) | ~$3,960 (est.) |
Prices shown are approximate and subject to change. VectorLay aims to offer H200 GPUs at competitive rates consistent with our mission to provide high-performance GPU compute at a fraction of hyperscaler pricing. Join the waitlist to be notified of pricing and availability.
Best Use Cases for the H200
The H200 is purpose-built for workloads where memory capacity and bandwidth are the primary bottleneck. If your models or workflows are constrained by the H100's 80GB, the H200 unlocks new possibilities:
Ultra-Large Model Inference
Run 70B+ parameter models at full precision on a single GPU without quantization. The H200's 141GB HBM3e fits Llama 3.1 70B with ample room for KV caches, enabling higher batch sizes and better throughput. Models that required 2x H100s for inference can often run on a single H200, simplifying deployment and reducing costs.
High-Throughput Inference Serving
The 4,800 GB/s memory bandwidth directly translates to faster token generation. For production APIs serving thousands of concurrent users, the H200 delivers significantly more tokens-per-second than the H100. The larger memory also allows bigger continuous batching windows, maximizing GPU utilization under heavy load.
Large-Batch Training
Train with larger batch sizes that fit entirely in the H200's 141GB memory, reducing the need for gradient accumulation steps and improving training throughput. Fine-tuning 70B+ models with full-precision optimizer states becomes feasible on a single GPU, eliminating the complexity of multi-GPU training for many use cases.
Multi-Modal AI Models
Large vision-language models that combine image encoders with language decoders often exceed 80GB when loaded with their full context. The H200's 141GB comfortably handles these multi-modal architectures, including models like LLaVA-Next, InternVL, and Gemini-class architectures that process both visual and textual inputs simultaneously.
Long-Context Applications
Long-context LLMs (128K+ tokens) generate massive KV caches that can easily consume 40–60GB of VRAM on top of the model weights. The H200's extra memory headroom allows you to serve these long-context models without sacrificing batch size, enabling production deployment of document analysis, code generation, and conversational agents that maintain extensive context windows.
Scientific Computing & Simulation
Large-scale simulations in molecular dynamics, climate modeling, and computational fluid dynamics benefit from the H200's expanded memory. Datasets and simulation states that previously required splitting across multiple GPUs can now fit on a single H200, reducing inter-GPU communication overhead and simplifying HPC workflows.
How to Get Access to the H200 on VectorLay
VectorLay is bringing H200 GPUs online soon. Here's how to get early access:
Join the waitlist
Visit vectorlay.com/contact to join the H200 waitlist. Tell us about your use case and expected workload so we can prioritize access for the most demanding applications.
Get notified on availability
We'll notify you as soon as H200 instances are available with confirmed pricing. Waitlist members get priority access and potential early-bird pricing.
Deploy with the same VectorLay experience
When available, the H200 will work exactly like every other GPU on VectorLay: VFIO passthrough for bare-metal performance, any Docker image, per-minute billing, and auto-failover. The full 141GB HBM3e will be exclusively yours—no sharing, no virtualization overhead.
Use H100s in the meantime
While waiting for the H200, VectorLay offers H100 SXM GPUs at $2.49/hr—the same Hopper architecture with 80GB HBM3. Many workloads that will benefit from the H200 can start development and testing on the H100 today.
H200 vs H100: Memory is the Upgrade
The H200 shares the H100's compute DNA but dramatically expands the memory envelope. Here's how they compare side-by-side:
| Feature | H200 SXM | H100 SXM |
|---|---|---|
| VRAM | 141GB HBM3e | 80GB HBM3 |
| Memory Bandwidth | 4,800 GB/s | 3,350 GB/s |
| Memory Type | HBM3e | HBM3 |
| FP16 Tensor | 989 TFLOPS | 989 TFLOPS |
| FP8 Tensor (sparsity) | 3,958 TFLOPS | 3,958 TFLOPS |
| Transformer Engine | Yes | Yes |
| NVLink | 4th Gen, 900 GB/s | 4th Gen, 900 GB/s |
| TDP | 700W | 700W |
The H200's value proposition is clear: same compute power, but 76% more memory and 43% more bandwidth. For inference workloads, NVIDIA reports the H200 delivers up to 1.9x higher throughput on Llama 2 70B compared to the H100, purely from the memory and bandwidth improvements. If your workload is memory-bound (which most LLM inference is), the H200 is a significant upgrade without requiring any code changes.
H200 Performance for AI Workloads
The H200's performance improvements over the H100 are most pronounced in memory-bound workloads. Here are representative benchmarks based on NVIDIA's published data:
| Workload | Model | H200 vs H100 |
|---|---|---|
| LLM Inference | Llama 2 70B | ~1.9x faster |
| LLM Inference | Mixtral 8x7B | ~1.6x faster |
| LLM Inference | GPT-3 175B (multi-GPU) | ~1.6x faster |
| Training | Large model fine-tuning | Larger batch sizes possible |
| Long-Context | 128K+ context windows | Fits larger KV caches |
The performance gains are driven almost entirely by the memory subsystem upgrade. More HBM3e means more model weights in fast memory, larger KV caches for higher batch sizes, and 43% more bandwidth to keep the compute units fed. For workloads that are already compute-bound on the H100, the H200 won't show significant improvements—but for the majority of LLM inference and training workloads that are memory-limited, the H200 is a substantial step forward.
Frequently Asked Questions
How much does it cost to rent an H200 in the cloud?
Cloud H200 pricing varies by provider. RunPod offers H200 GPUs at approximately $3.89/hr, Lambda Labs at around $3.49/hr, and CoreWeave at roughly $3.29/hr. AWS is estimated at around $5.50/hr for H200 instances. VectorLay H200 pricing is coming soon—join the waitlist to be notified when availability and pricing are finalized.
What is the difference between the H200 and H100?
The H200 is a direct upgrade to the H100, built on the same Hopper architecture but with significantly more memory. The H200 features 141GB of HBM3e memory (vs 80GB HBM3 on the H100) and 4,800 GB/s of memory bandwidth (vs 3,350 GB/s). This 1.8x increase in memory capacity and 1.4x increase in bandwidth make the H200 substantially faster for memory-bound workloads like large language model inference. The compute capabilities (CUDA cores, Tensor Cores, Transformer Engine) remain largely the same.
What are the best use cases for the H200?
The H200 excels at workloads that are memory-capacity or memory-bandwidth limited. Running very large language models like Llama 3.1 405B (which requires multiple H100s but fewer H200s), serving high-throughput inference with large KV caches, training models that benefit from larger batch sizes that fit in memory, and any application where the 80GB limit of the H100 is a bottleneck. The extra 61GB of VRAM can eliminate the need for model parallelism in many cases.
When will the H200 be available on VectorLay?
VectorLay is actively working to bring H200 GPUs to the platform. Availability is coming soon. Join the waitlist at vectorlay.com/contact to be the first to know when H200 instances are available, and to lock in early pricing. We expect H200 pricing to be competitive with or below other cloud GPU providers.
Is the H200 worth the premium over the H100?
For memory-bound workloads, absolutely. The H200's 141GB HBM3e allows you to run models that would require multi-GPU setups on the H100 on a single GPU, which is both simpler and more cost-effective. For compute-bound workloads where the H100's 80GB is sufficient, the H100 may still offer better value. The H200 is particularly compelling for inference serving, where larger KV caches translate directly to higher throughput and lower latency.
How does the H200 compare to the B100 and B200?
The H200 is based on the Hopper architecture, while the B100 and B200 are based on the newer Blackwell architecture. Blackwell GPUs offer a generational leap in compute performance with 2nd-gen Transformer Engine and new FP4 precision support. However, the H200 is available now from select providers, while Blackwell data center GPUs are still ramping up availability. For teams that need maximum memory today, the H200 is the immediate solution.
Ready for 141GB of HBM3e?
The H200 is coming to VectorLay. Join the waitlist to get priority access, early-bird pricing, and be the first to deploy on the most memory-dense GPU ever built.