NVIDIA32 GB VRAM900 GB/s$0.5/hr

Mistral 7B v0.3 on V100 SXM2 32GB: 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 V100 SXM2 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
23.1 GB needed / 32 GB available
Decode speed
40.40
tok/s per stream
Aggregate
484.8
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2865
1× V100 SXM2 32GB
What changes as you add V100 SXM2 32GBs
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× V10032 GB23.1 GB40.40484.81.95 s$0.2865✓
2× V10064 GB23.1 GB68.681648993.5 ms$0.1685✓
4× V100128 GB23.1 GB137.46593511.8 ms$0.0843✓
8× V100256 GB23.1 GB274.726373270.9 ms$0.0421✓
Concurrency vs context length
On 1× V100 SXM2 32GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K32549.8 MB✓ fits
8K161.07 GB✓ fits
33K44.29 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
A100 40GB SXM41×834.9$0.2994
AMD MI325X 288GB1×3232$0.3008
Frequently asked questions

Can Mistral 7B v0.3 run on V100 SXM2 32GB?

Yes. Mistral 7B v0.3 at FP16 needs 23.1 GB including the KV cache, and V100 SXM2 32GB has 32 GB — so it fits on a single GPU.

How fast is Mistral 7B v0.3 on V100 SXM2 32GB?

40.40 tokens/sec per stream and 484.8 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.95 s, and inter-token latency is 24.8 ms.

What does Mistral 7B v0.3 on V100 SXM2 32GB cost per million tokens?

$0.2865 per 1M output tokens at 1× V100 SXM2 32GB ($0.5000/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 V100 SXM2 32GB?

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 V100 SXM2 32GB the right choice for Mistral 7B v0.3?

It works. Compare $0.2865 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