TL;DR: H200 offers 76% more memory (141GB vs 80GB) and 43% more bandwidth at the same compute and power. Choose H200 for 70B+ models or long-context applications. Choose H100 for smaller models or budget-constrained deployments.
The Key Differences
At first glance, comparing H200 and H100 seems straightforward:
| Specification | H100 SXM | H200 SXM | Difference |
|---|---|---|---|
| GPU Memory | 80 GB HBM3 | 141 GB HBM3e | +76% |
| Memory Bandwidth | 3.35 TB/s | 4.8 TB/s | +43% |
| FP16 TFLOPS | 990 | 990 | Same |
| TDP | 700W | 700W | Same |
Key insight: The H200 offers 76% more memory and 43% more bandwidth at the same compute performance and power envelope.
When H200 Makes Sense
Large Language Models
If you're deploying models like Llama 70B or larger:
| Benefit | Impact |
|---|---|
| Single GPU can hold larger models | Simpler architecture |
| Fewer GPUs required for inference | Lower total cost |
| Simplified deployment | Easier operations |
Long Context Applications
KV cache grows with context length. For applications requiring 100K+ token contexts:
| Benefit | Impact |
|---|---|
| Larger memory prevents OOM errors | More reliable |
| Maintains performance at longer contexts | Better UX |
| Enables batch processing of long documents | Higher throughput |
Research and Experimentation
When model architecture is still evolving:
| Benefit | Impact |
|---|---|
| Memory headroom for larger experiments | Flexibility |
| Scale up without hardware changes | Future-proofing |
| Accommodates model size growth | Longevity |
When H100 Makes Sense
Budget-Constrained Deployments
H100 costs 15-20% less than H200. If your models fit in 80GB:
| Benefit | Impact |
|---|---|
| Lower capital expenditure | Preserve cash |
| Faster ROI on investment | Quicker payback |
| More GPUs for the same budget | More compute |
Small-to-Medium Models
For 7B-13B parameter models:
| Benefit | Impact |
|---|---|
| 80GB is more than sufficient | No wasted capacity |
| Memory bandwidth adequate | Full performance |
| H200's extra memory provides no benefit | Avoid overspending |
Training Workloads
For training where batch size isn't memory-limited:
| Benefit | Impact |
|---|---|
| Compute performance is identical | Same training speed |
| Memory bandwidth difference is minimal | Negligible impact |
| Cost savings can fund additional nodes | More parallelism |
Real-World Performance
Benchmark: Llama 2 70B inference
| Metric | H100 (2 GPU) | H200 (1 GPU) | Winner |
|---|---|---|---|
| Throughput (tokens/sec) | 85 | 92 | H200 |
| Latency (TTFT) | 45ms | 38ms | H200 |
| Power Draw | 1,400W | 700W | H200 |
| TCO (3 year) | $1.2M | $800K | H200 |
Result: The H200 single-GPU deployment achieves better performance at lower cost for this specific workload.
Pricing Analysis and ROI
Hardware Pricing (January 2026)
| Configuration | H100 SXM | H200 SXM | Premium |
|---|---|---|---|
| Single GPU (standalone) | ~$25,000 | ~$30,000 | 20% |
| 8-GPU Server (DGX class) | ~$280,000 | ~$340,000 | 21% |
| 8-GPU Server (OEM) | ~$200,000 | ~$250,000 | 25% |
Note: Prices fluctuate with availability. Contact SLYD for current quotes.
Cost Per Inference Token
Workload: Llama 70B inference (batch size 8, 512 token output)
| Metric | H100 (2 GPU) | H200 (1 GPU) | Difference |
|---|---|---|---|
| Throughput | 85 tokens/sec | 92 tokens/sec | +8% |
| Hardware Cost | $50,000 | $30,000 | -40% |
| Power (annual) | $7,350 | $3,675 | -50% |
| Cost per 1M tokens | $0.032 | $0.024 | -25% |
Bottom line: H200 delivers 25% lower cost per token for this workload despite higher per-GPU price.
Break-Even Analysis
| Factor | Value |
|---|---|
| Price premium | ~$5,000 per GPU |
| Power savings | ~$3,675/year (single GPU vs dual) |
| Break-even | ~16 months |
For workloads where H200 eliminates the need for a second GPU, the premium pays for itself in under 2 years through power savings alone.
Use Case Decision Matrix
| Workload | Model Size | Recommended | Why |
|---|---|---|---|
| Chatbot (7B) | 14GB | H100 | Memory sufficient, lower cost |
| Chatbot (13B) | 26GB | H100 | Comfortable memory headroom |
| Chatbot (70B) | 140GB | H200 | Single GPU, simpler deployment |
| RAG (13B + embeddings) | 40GB | H100 | Memory sufficient |
| RAG (70B + long context) | 180GB+ | H200 × 2 | KV cache requires capacity |
| Training (7B) | 50GB active | H100 | Compute-bound, not memory |
| Training (70B) | 400GB+ | H200 cluster | Memory helps with batch size |
| Fine-tuning (70B, LoRA) | 100GB | H200 | Single GPU possible |
| Fine-tuning (70B, full) | 400GB+ | Either (cluster) | Multi-GPU required regardless |
Power and Cooling Comparison
Both GPUs have identical TDP (700W), but deployment differences affect total facility power.
Single Large Model Deployment
| Aspect | H100 Solution | H200 Solution | Savings |
|---|---|---|---|
| GPUs Required | 2 | 1 | 50% |
| GPU Power | 1,400W | 700W | 50% |
| Server Overhead | 800W | 400W | 50% |
| Total Power | 2,200W | 1,100W | 50% |
| Cooling (PUE 1.4) | 3,080W | 1,540W | 50% |
| Monthly Power Cost | ~$330 | ~$165 | 50% |
Key insight: H200 cuts power consumption in half when it eliminates the need for multi-GPU deployment.
Cooling Requirements
| Solution | H100 | H200 |
|---|---|---|
| Air cooling | Possible but loud, limits density | Same |
| Direct liquid cooling | Recommended for production | Same |
| Rear-door heat exchangers | Alternative for air-cooled facilities | Same |
The difference: Deploying half as many GPUs simplifies cooling infrastructure proportionally.
Frequently Asked Questions
Is the H200 worth the premium for training?
Usually no. Training is compute-bound more than memory-bound.
| Consideration | Analysis |
|---|---|
| Price premium | 20-25% higher cost |
| Training speedup | Minimal (compute is identical) |
| Memory benefit | Helps with batch sizes, but limited impact |
Exception: If you're training models that require gradient checkpointing on H100 but fit without checkpointing on H200, the memory advantage could improve training throughput meaningfully.
Can I mix H100 and H200 in the same cluster?
Yes, with caveats. Same architecture means software compatibility. However:
| Consideration | Impact |
|---|---|
| Different memory sizes | Complicates tensor parallelism |
| Workload segmentation | You'll likely want inference on H200, training on H100 |
| NVLink bridges | Work within server, not across |
Recommendation: Homogeneous clusters are simpler to manage and optimize.
Should I wait for B200 instead?
The B200 offers significant improvements:
| Spec | B200 | H200 | Improvement |
|---|---|---|---|
| Memory | 192GB HBM3e | 141GB HBM3e | +36% |
| Bandwidth | 8 TB/s | 4.8 TB/s | +67% |
| FP16 TFLOPS | 2,250 | 990 | +127% |
| TDP | 1,000W | 700W | +43% |
| Wait If... | Don't Wait If... |
|---|---|
| You're 9+ months from production | You have immediate production needs |
| You need the compute improvement for training | H200 memory is sufficient for your models |
| Your facility can handle 1kW/GPU power | Your facility is power-constrained |
How does the memory advantage affect multi-turn conversations?
Each conversation turn adds to KV cache. For long conversations:
| Context Length | KV Cache (Llama 70B) | H100 Remaining | H200 Remaining |
|---|---|---|---|
| 4K tokens | 4GB | 36GB | 97GB |
| 16K tokens | 16GB | 24GB | 85GB |
| 32K tokens | 32GB | 8GB | 69GB |
| 64K tokens | 64GB | Insufficient | 37GB |
Key insight: H200's extra memory directly translates to longer supported context or more concurrent conversations.
Making the Decision
| Question | If Yes → | If No → |
|---|---|---|
| Is your largest model >70GB? | H200 | H100 |
| Will your models grow significantly? | H200 | H100 |
| Do you need long context (>32K)? | H200 | H100 |
| Is budget your primary constraint? | H100 | Consider H200 |
Conclusion
There's no universal "better" choice between H200 and H100. The right GPU depends on your specific workloads, growth plans, and budget constraints. Consider starting with a pilot deployment to gather real performance data before committing to fleet purchases.