H100 SXM vs GH200
Both run on the same nodes, the same templates and the same tiers here — so the choice comes down to memory, bandwidth and price. Every figure below is from the catalogue or the vendor datasheet.
- H100
- $0.55/h80 GB · Hopper · 96 available
- GH200
- $0.49/h96 GB · Hopper · 16 available
- Price gap
- 11%On spot, per GPU-hour
- Over 100 h
- $6.00Difference for the same GPU-hours
What actually separates them.
Four differences that change a decision, computed from the numbers rather than asserted.
Bigger models fit on the GH200
96 GB against 80 GB. At FP16 that is about 38B parameters on one GPU, against 32B.
The GH200 costs 11% less per hour
$0.49 against $0.55 on spot — $6.00 of difference over 100 GPU-hours.
Token generation is faster on the GH200
4,000 GB/s against 3,350 GB/s. Inference reads the whole model from memory for every token, so bandwidth, not FLOPS, usually sets the ceiling.
More memory per dollar on the GH200
196 GB per dollar per hour against 145 GB. The usual tie-breaker when both cards fit the job.
Every figure we hold on both cards.
A row is marked only when both values exist and one is genuinely better. H100 leads on 2 rows, GH200 on 7.
| H100 SXM | GH200 | |
|---|---|---|
| GPU memoryDecides which models fit at all | 80 GB HBM3 | 96 GB HBM3 |
| Memory bandwidthSets token throughput on inference | 3,350 GB/s | 4,000 GB/s |
| FP16 tensor | 990 TFLOPS | 990 TFLOPS |
| FP32 | 67 TFLOPS | 67 TFLOPS |
| 8-bit float (FP8)Halves the memory a model needs, where the stack supports it | Supported | Supported |
| Architecture | Hopper (2022) | Hopper (2024) |
| Board powerLower is cheaper to run at scale — for us, not for your bill | 700 W | 1000 W |
| GPU-to-GPU linkOnly matters for multi-GPU training | NVLink | NVLink |
| Node sizes | 1×, 2×, 4×, 8× | 1× |
| Spot price | $0.55/h | $0.49/h |
| On-demand price | $0.89/h | $0.79/h |
| Memory per dollarGPU memory per $1 of spot time, per hour | 145 GB | 196 GB |
| 30-day reclaim rateShare of spot instances reclaimed | <5% | 5–10% |
| Available now | 96 GPUs | 16 GPUs |
Specifications come from each vendor's datasheet — linked on the H100 page and the GH200 page. Prices are ours, per GPU-hour, billed per minute.
The same models, on one GPU of each.
Weights only, with 20% of the memory kept free for activations and the KV cache. A cross means one GPU is not enough — the model page has the multi-GPU arithmetic.
| Model | Precision | H100 80 GB | GH200 96 GB |
|---|---|---|---|
| Llama 3.1 8B8B parameters | FP16 · 16 GB | Fits | Fits |
| FP8 · 8 GB | Fits | Fits | |
| INT4 · 4 GB | Fits | Fits | |
| Qwen2.5 32B32B parameters | FP16 · 64 GB | Fits | Fits |
| FP8 · 32 GB | Fits | Fits | |
| INT4 · 16 GB | Fits | Fits | |
| Llama 3.3 70B70B parameters | FP16 · 140 GB | Needs 4× node | Needs more than one node |
| FP8 · 70 GB | Needs 2× node | Fits | |
| INT4 · 35 GB | Fits | Fits | |
| Mixtral 8x22B141B parameters | FP16 · 282 GB | Needs 8× node | Needs more than one node |
| FP8 · 141 GB | Needs 4× node | Needs more than one node | |
| INT4 · 71 GB | Needs 2× node | Fits |
The same 100 GPU-hours on each.
Compute only, billed per minute. Storage is $0.08 per GB-month on either card.
| H100 | GH200 | |
|---|---|---|
| One hour, spot | $0.55 | $0.49 |
| One day, spot | $13.20 | $11.76 |
| 100 GPU-hours, spot | $55.00 | $49.00 |
| 100 GPU-hours, on-demand | $89.00 | $79.00 |
| A month, reserved | $474.50 | $430.70 |
Take the H100 when…
- Training and full fine-tuning — Enough memory for optimiser states and activations on models a smaller card can only run in inference.
- Serving large models — A 70B model at FP8 across two GPUs, with room for a long context window.
- Multi-GPU jobs over NVLink — GPU-to-GPU traffic stays off the PCIe bus, which is what makes tensor and pipeline parallelism worth it.
Take the GH200 when…
- Training and full fine-tuning — Enough memory for optimiser states and activations on models a smaller card can only run in inference.
- Serving large models — A 70B model at FP8 across two GPUs, with room for a long context window.
- Multi-GPU jobs over NVLink — GPU-to-GPU traffic stays off the PCIe bus, which is what makes tensor and pipeline parallelism worth it.
H100 or GH200?
Which is cheaper, the H100 or the GH200?
The GH200, at $0.49 per GPU-hour on spot against $0.55 — 11% less. On-demand: $0.79 against $0.89.
Which one should I take for inference?
Take the one that holds the model with room for the KV cache, then the one with more memory bandwidth. Here that is the GH200 on capacity (96 GB) and the GH200 on bandwidth.
Can I run both on the same job?
Not inside one instance — a node holds GPUs of a single model. You can run two instances in parallel, on the same persistent volume in turn, or split the work: a big card for the model that needs capacity, a cheap one for the parts that do not.
Other comparisons: the full list · all 47 models
Try both for less than the price of deciding.
An hour on each costs $1.04 on spot. Nothing renews, and your disk moves between them.
Billed per minute from the moment the instance is reachable. Minimum credit $40, no subscription.