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

Mistral 7B v0.3 on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Mistral 7B v0.3 at FP16 needs 14.5 GB for its weights and 23.1 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
23.1 GB needed / 80 GB available
Decode speed
150.4
tok/s per stream
Aggregate
1805
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3848
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 GB23.1 GB150.41805276.3 ms$0.3848✓
2× H100160 GB23.1 GB255.66135153.7 ms$0.2264✓
4× H100320 GB23.1 GB511.32454291.9 ms$0.1132✓
8× H100640 GB23.1 GB10239816860.9 ms$0.0566✓
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?
4K122549.8 MB✓ fits
8K611.07 GB✓ fits
33K154.29 GB✓ fits
66K78.59 GB✓ fits
131K317.18 GB✓ fits
Other GPUs that run Mistral 7B v0.3
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×504.2$0.1377
RTX 5090 32GB1×965.3$0.1439
RTX 4090 24GB1×543.0$0.2046
AMD MI300X 192GB1×2855$0.2432
V100 SXM2 32GB1×484.8$0.2865
A100 40GB SXM41×834.9$0.2994
Frequently asked questions

Can Mistral 7B v0.3 run on H100 SXM5 80GB?

Yes. Mistral 7B v0.3 at FP16 needs 23.1 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Mistral 7B v0.3 on H100 SXM5 80GB?

150.4 tokens/sec per stream and 1805 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 276.3 ms, and inter-token latency is 6.6 ms.

What does Mistral 7B v0.3 on H100 SXM5 80GB cost per million tokens?

$0.3848 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 Mistral 7B v0.3 need on H100 SXM5 80GB?

14.5 GB for the weights at FP16, plus 8.6 GB of KV cache for 8 concurrent requests at 8K context — 23.1 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Mistral 7B v0.3?

It works. Compare $0.3848 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0399, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10773 tok/s.

Adjust the workload yourself

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

Open in calculator