The NVIDIA H200 exists for one very specific reason. Modern AI workloads are no longer compute-bound; they are memory-bound. Large language models, long-context inference, retrieval-augmented generation, and multi-modal pipelines all hit memory limits before they hit raw FLOPS. The H200 solves that problem by pushing memory capacity and bandwidth far beyond what H100 offers, while keeping the same Hopper architecture and software ecosystem.
Spheron gives teams direct access to H200 GPUs across spot, dedicated, and reserved configurations. You do not need to negotiate with hyperscalers, wait for allocation windows, or lock yourself into long contracts. If your models no longer fit cleanly on H100, H200 is not a luxury upgrade; it is the correct tool.
Why H200 Exists
From the outside, H200 looks like a small step from H100. Same Hopper architecture. Same Tensor Cores. Similar power envelope. The difference is memory.
H200 ships with 141 GB of HBM3e memory and 4.8 TB/s of memory bandwidth. This represents a 76% jump in capacity and over 40% more bandwidth compared to H100. For memory-bound workloads, this changes everything.
Models that previously required tensor parallelism across multiple H100s now fit on fewer GPUs. Inference pipelines can run larger batch sizes without spilling to host memory. Long-context workloads stop thrashing memory and start behaving predictably. This is why H200 often delivers up to 1.9x faster LLM inference than H100, even though raw compute remains nearly identical.
H200 Technical Specifications
| Specification | H200 SXM | H100 SXM (comparison) |
|---|---|---|
| Architecture | Hopper (GH100) | Hopper (GH100) |
| Process Node | TSMC 4N | TSMC 4N |
| CUDA Cores | 16,896 | 16,896 |
| Tensor Cores | 528 (4th Gen) | 528 (4th Gen) |
| VRAM | 141 GB HBM3e | 80 GB HBM3 |
| Memory Bandwidth | 4,800 GB/s | 3,350 GB/s |
| FP64 (TFLOPS) | 34 | 34 |
| FP32 (TFLOPS) | 67 | 67 |
| TF32 Tensor (TFLOPS) | 989 | 989 |
| BF16/FP16 Tensor (TFLOPS) | 1,979 | 1,979 |
| FP8 Tensor (TFLOPS) | 3,958 | 3,958 |
| INT8 Tensor (TOPS) | 3,958 | 3,958 |
| NVLink Bandwidth | 900 GB/s | 900 GB/s |
| PCIe | Gen5 (128 GB/s) | Gen5 (128 GB/s) |
| MIG Instances | 7x 16.5 GB | 7x 10 GB |
| TDP | 700W | 700W |
Every compute metric is identical. The H200's advantage is entirely in memory: 76% more capacity and 43% more bandwidth. This makes the H200 a targeted upgrade for memory-bound workloads, not a general-purpose replacement.
Model Capacity: H200 vs H100
The H200's expanded memory changes which models fit on a single GPU:
| Model | Parameters | VRAM (FP16) | H100 80GB | H200 141GB |
|---|---|---|---|---|
| Llama 3.1 8B | 8B | 16 GB | Yes | Yes |
| Mistral 7B | 7B | 14 GB | Yes | Yes |
| Llama 2 13B | 13B | 26 GB | Yes | Yes |
| Llama 2 70B (FP16) | 70B | 140 GB | No (2 GPU) | Yes (tight) |
| Llama 2 70B (INT8) | 70B | 70 GB | Yes (tight) | Yes |
| Mixtral 8x7B | 47B | 94 GB | No (2 GPU) | Yes |
| Falcon 180B (INT4) | 180B | ~90 GB | No (2 GPU) | Yes |
| Llama 3.1 405B | 405B | 810 GB | 8 GPU (TP8) | 6 GPU (TP6) |
The defining advantage: H200 serves Llama 70B and Mixtral 8x7B on a single GPU in FP16. These are workloads that require 2 H100s. This halves GPU count and eliminates inter-GPU communication overhead.
Inference Benchmarks
MLPerf Results (Llama 2 70B)
| Scenario | H100 SXM (tok/s) | H200 SXM (tok/s) | H200 Improvement |
|---|---|---|---|
| Offline (max throughput) | 22,290 | 31,712 | +42.3% |
| Server (latency-bound) | 21,504 | 29,526 | +37.3% |
| Max throughput (single GPU) | Baseline | 1.9x | 1.9x |
Throughput by Model Size
| Workload | H200 vs H100 Speedup | Why |
|---|---|---|
| Llama 2 13B | ~1.5x | Moderate memory benefit |
| Llama 2 70B (max throughput) | 1.9x | Memory-dominated decode |
| GPT-3 175B (8 GPU, online) | 1.6x | More KV cache headroom |
| Llama 3.1 405B (8 GPU, pipeline) | 1.5x | Fewer pipeline stages needed |
The larger the model, the greater the H200's advantage. For models under 13B parameters, the memory bandwidth difference is less impactful and the H100 delivers similar per-token latency at a lower price.
H200 Is Built for Inference First
H200 can train large models, but its real strength shows up in inference and memory-heavy workloads. With 141 GB available per GPU, H200 allows you to keep more weights, KV cache, and intermediate activations on-device. This directly improves token throughput and reduces tail latency. It also simplifies system design: you need fewer GPUs, less sharding logic, and fewer failure points.
For training, H200 shines when batch sizes or model states exceed H100 limits. It does not replace B200 or multi-node Blackwell systems for massive pre-training, but for fine-tuning, continued training, and research-scale runs, it removes painful memory constraints.
H200 Configurations on Spheron
Spheron provides NVIDIA H200 GPUs across multiple deployment models.
Reserved HGX Cluster
The reserved H200 offering is delivered as a full NVIDIA HGX H200 8-GPU SXM system, designed for sustained, high-throughput AI workloads. The hardware includes dual Intel Xeon Platinum 8468+ processors with 2 TB of system memory, 2x 980 GB SSDs for the OS, and 2x 7.68 TB NVMe drives for data. Networking includes eight CX7 NDR 400 Gbps adapters for distributed training, a CX6 HDR 200 Gbps adapter, and a dual-port 10 Gbps management adapter.
| Commitment | Price per GPU per Hour |
|---|---|
| 1 month | $1.95/hr |
| 3 months | $1.85/hr |
| 6 months | $1.80/hr |
Spot Instances
Spot instances provide the lowest-cost access to H200 capacity for fault-tolerant workloads. Best available spot price: $1.87/hr per GPU. Typical configuration: ~44 vCPUs, 182 GB RAM, 200 GB storage (SXM5 interconnect).
Dedicated Instances
Dedicated H200 SXM instances guarantee capacity and stable performance without interruption.
| GPU Count | Starting Price |
|---|---|
| 1x GPU | From $3.23/hr |
| 2x GPUs | From $7.34/hr |
| 4x GPUs | From $14.52/hr |
| 8x GPU cluster | From $31.68/hr |
Pricing starts at $3.23/hr per GPU through Sesterce (8 regions) and $3.75/hr through DataCrunch (2 regions).
When to Use Each Tier
Spot H200 instances work well for research, experimentation, and burst inference. They are cost-effective but not guaranteed.
Dedicated H200 instances suit production workloads that need stability without long commitments. You pay more per hour but avoid interruptions.
Reserved H200 clusters make sense when your workload runs continuously. Long-term reservations bring per-hour pricing down to $1.80/hr and give you full control over hardware.
Networking and Scaling
H200 systems on Spheron support 200G and 400G networking fabrics in reserved configurations. This matters when you scale inference across multiple GPUs or nodes.
Memory-heavy inference pipelines often saturate interconnects before compute. Proper networking ensures that tensor parallelism and pipeline parallelism do not collapse under load. Spheron exposes system-level details so you can make informed decisions about bandwidth and topology.
Cost Efficiency: Fewer GPUs, Lower TCO
One mistake teams make is comparing hourly GPU prices in isolation. H200 often reduces total cost because you need fewer GPUs. Larger memory means fewer shards, higher bandwidth leads to higher utilization, and better throughput means fewer replicas.
| Workload | H100 Configuration | H200 Configuration | H200 Savings |
|---|---|---|---|
| Llama 70B inference (FP16) | 2x H100 ($6.00/hr) | 1x H200 ($3.23/hr) | ~46% |
| Mixtral 8x7B inference | 2x H100 ($6.00/hr) | 1x H200 ($3.23/hr) | ~46% |
| 405B serving | 8x H100 ($24.00/hr) | 6x H200 ($19.38/hr) | ~19% |
When you factor in system complexity, orchestration overhead, and engineering time, H200 frequently wins for large inference systems even when the per-GPU hourly rate looks higher.
Software Compatibility
H200 runs on the same Hopper software stack as H100. CUDA, cuDNN, TensorRT-LLM, PyTorch, JAX, and vLLM all work without changes. Teams can move from H100 to H200 without rewriting pipelines or retraining staff. The performance gain comes from hardware, not refactoring.
Getting Started with H200 on Spheron
Deploying takes only a few minutes:
- Sign Up: Head to app.spheron.ai and sign up with GitHub or Gmail
- Add Credits: Click the credit button in the top-right corner. You can pay with card or crypto
- Start Deployment: Click "Deploy" in the left-hand menu to see the GPU catalog
- Configure Your Instance: Select H200, choose vCPUs, RAM, storage, region, and Ubuntu 22.04
- Review and Deploy: Check the order summary, add your SSH key, click "Deploy Instance"
Within a minute, your GPU VM will be ready with full root SSH access:
ssh -i <private-key-path> sesterce@<your-vm-ip>When H200 Is the Right Choice
Choose H200 if you run large LLM inference, long-context workloads, retrieval-heavy systems, or memory-bound training. Choose it if H100 feels cramped even after optimization.
Do not choose H200 just because it is newer. If your workloads are compute-bound or small enough to fit on H100, you will not see meaningful gains. Spheron supports both, which means you can test, measure, and choose based on data.
Explore GPU options on Spheron →
Frequently Asked Questions
How much faster is H200 than H100 for LLM inference?
H200 delivers 37 to 90% faster inference depending on the model and serving configuration. For Llama 2 70B, MLPerf benchmarks show 42% higher throughput in offline mode and 37% in server mode. For maximum single-GPU throughput, H200 achieves 1.9x the H100's performance.
Can H200 serve Llama 70B on a single GPU?
Yes. Llama 70B in FP16 requires approximately 140 GB of VRAM. The H200's 141 GB fits the full model on one GPU, while the H100's 80 GB requires 2-way tensor parallelism. This single-GPU approach halves hardware cost and eliminates inter-GPU communication latency.
Is H200 worth the premium over H100?
For memory-bound workloads (70B+ models, long-context serving, multi-tenant MIG), yes. The H200's higher throughput often delivers lower cost-per-token even at a higher hourly rate. For compute-bound workloads or models under 30B parameters, the H100 provides equivalent performance at a lower price.
What's the difference between spot and dedicated H200 on Spheron?
Spot H200 instances ($1.87/hr) offer the lowest price but can be interrupted. They suit fault-tolerant research and experimentation. Dedicated instances ($3.23/hr+) guarantee availability for production workloads. Reserved clusters ($1.80–$1.95/hr) provide the best per-hour pricing for continuous workloads with commitment periods.
Does H200 require different software than H100?
No. H200 runs the same CUDA, cuDNN, TensorRT-LLM, PyTorch, JAX, and vLLM stack as H100. No code changes, no recompilation, no pipeline rewrites. The performance improvement comes entirely from the hardware memory upgrade.
Should I wait for B200 instead of renting H200?
B200 offers 2.3x the compute and 1.7x the bandwidth of H200 with 192 GB HBM3e. However, Blackwell availability is limited and pricing is substantially higher. If you need GPU capacity now, H200 provides the best available memory-per-dollar for LLM workloads. Spheron supports both generations, so you can migrate when Blackwell pricing becomes competitive.