NVIDIA80 GB VRAM3,350 GB/s$2.5/hr

Gemma 2 9B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Gemma 2 9B at FP16 needs 18.0 GB for its weights and 40.5 GB in total at 8K context with 8 concurrent users. That means it fits on a single H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
40.5 GB needed / 80 GB available
Decode speed
121.0
tok/s per stream
Aggregate
1452
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4784
1× H100 SXM5 80GB
What changes as you add H100 SXM5 80GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× H10080 GB40.5 GB121.01452336.2 ms$0.4784✓
2× H100160 GB40.5 GB205.74936183.8 ms$0.2814✓
4× H100320 GB40.5 GB411.319743106.9 ms$0.1407✓
8× H100640 GB40.5 GB822.67897168.5 ms$0.0703✓
Concurrency vs context length
On 1× H100 SXM5 80GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K431.41 GB✓ fits
8K212.82 GB✓ fits
33K511.27 GB✓ fits
66K222.55 GB✓ fits
131K145.10 GB✓ fits
Other GPUs that run Gemma 2 9B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2640$0.1052
AMD MI300X 192GB1×2297$0.3024
AMD MI325X 288GB1×2600$0.3739
A100 80GB SXM41×866.7$0.3846
Intel Gaudi 3 128GB1×1430$0.3885
B200 SXM 192GB1×3467$0.4808
Frequently asked questions

Can Gemma 2 9B run on H100 SXM5 80GB?

Yes. Gemma 2 9B at FP16 needs 40.5 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Gemma 2 9B on H100 SXM5 80GB?

121.0 tokens/sec per stream and 1452 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 336.2 ms, and inter-token latency is 8.3 ms.

What does Gemma 2 9B on H100 SXM5 80GB cost per million tokens?

$0.4784 per 1M output tokens at 1× H100 SXM5 80GB ($2.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Gemma 2 9B need on H100 SXM5 80GB?

18.0 GB for the weights at FP16, plus 22.5 GB of KV cache for 8 concurrent requests at 8K context — 40.5 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Gemma 2 9B?

It works. Compare $0.4784 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0248, and the fastest is 8× RTX 3080 10GB at 17916 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator