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

Llama 2 7B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Llama 2 7B at FP16 needs 13.5 GB for its weights and 47.8 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
47.8 GB needed / 80 GB available
Decode speed
161.5
tok/s per stream
Aggregate
1938
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3583
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 GB47.8 GB161.51938259.3 ms$0.3583✓
2× H100160 GB47.8 GB274.66591145.2 ms$0.2107✓
4× H100320 GB47.8 GB549.22636387.6 ms$0.1054✓
8× H100640 GB47.8 GB109810545058.8 ms$0.0527✓
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?
4K302.15 GB✓ fits
8K154.29 GB✓ fits
33K317.18 GB✓ fits
66K134.36 GB✓ fits
131K068.72 GB✗ does not fit
Other GPUs that run Llama 2 7B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×3526$0.0788
AMD MI300X 192GB1×3067$0.2264
AMD MI325X 288GB1×3472$0.2800
A100 80GB SXM41×1157$0.2880
Intel Gaudi 3 128GB1×1909$0.2909
B200 SXM 192GB1×4629$0.3600
Frequently asked questions

Can Llama 2 7B run on H100 SXM5 80GB?

Yes. Llama 2 7B at FP16 needs 47.8 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Llama 2 7B on H100 SXM5 80GB?

161.5 tokens/sec per stream and 1938 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 259.3 ms, and inter-token latency is 6.2 ms.

What does Llama 2 7B on H100 SXM5 80GB cost per million tokens?

$0.3583 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 Llama 2 7B need on H100 SXM5 80GB?

13.5 GB for the weights at FP16, plus 34.4 GB of KV cache for 8 concurrent requests at 8K context — 47.8 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Llama 2 7B?

It works. Compare $0.3583 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0186, and the fastest is 2× Cerebras CS-3 (WSE-3) at 39347 tok/s.

Adjust the workload yourself

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

Open in calculator