NVIDIA40 GB VRAM1,550 GB/s$0.9/hr

Mistral 7B v0.3 on A100 40GB SXM4: 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 A100 40GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
23.1 GB needed / 40 GB available
Decode speed
69.58
tok/s per stream
Aggregate
834.9
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2994
1× A100 40GB SXM4
What changes as you add A100 40GB SXM4s
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× A10040 GB23.1 GB69.58834.9804.8 ms$0.2994✓
2× A10080 GB23.1 GB118.32839418.6 ms$0.1761✓
4× A100160 GB23.1 GB236.611355224.3 ms$0.0881✓
8× A100320 GB23.1 GB473.145421127.2 ms$0.0440✓
Concurrency vs context length
On 1× A100 40GB SXM4 at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K47549.8 MB✓ fits
8K231.07 GB✓ fits
33K54.29 GB✓ fits
66K28.59 GB✓ fits
131K117.18 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
AMD MI325X 288GB1×3232$0.3008
Frequently asked questions

Can Mistral 7B v0.3 run on A100 40GB SXM4?

Yes. Mistral 7B v0.3 at FP16 needs 23.1 GB including the KV cache, and A100 40GB SXM4 has 40 GB — so it fits on a single GPU.

How fast is Mistral 7B v0.3 on A100 40GB SXM4?

69.58 tokens/sec per stream and 834.9 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 804.8 ms, and inter-token latency is 14.4 ms.

What does Mistral 7B v0.3 on A100 40GB SXM4 cost per million tokens?

$0.2994 per 1M output tokens at 1× A100 40GB SXM4 ($0.9000/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 A100 40GB SXM4?

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 A100 40GB SXM4 the right choice for Mistral 7B v0.3?

It works. Compare $0.2994 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