NVIDIA24 GB VRAM600 GB/s$0.36/hr

Mistral 7B v0.3 on A10G 24GB: 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 A10G 24GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
23.1 GB needed / 24 GB available
Decode speed
26.93
tok/s per stream
Aggregate
323.2
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3094
1× A10G 24GB
What changes as you add A10G 24GBs
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× A10G24 GB23.1 GB26.93323.27.72 s$0.3094✓
2× A10G48 GB23.1 GB45.7910993.88 s$0.1820✓
4× A10G96 GB23.1 GB91.5743961.95 s$0.0910✓
8× A10G192 GB23.1 GB183.117582992.1 ms$0.0455✓
Concurrency vs context length
On 1× A10G 24GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K17549.8 MB✓ fits
8K81.07 GB✓ fits
33K24.29 GB✓ fits
66K18.59 GB✓ fits
131K017.18 GB✗ does not fit
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 A10G 24GB?

Yes. Mistral 7B v0.3 at FP16 needs 23.1 GB including the KV cache, and A10G 24GB has 24 GB — so it fits on a single GPU.

How fast is Mistral 7B v0.3 on A10G 24GB?

26.93 tokens/sec per stream and 323.2 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 7.72 s, and inter-token latency is 37.1 ms.

What does Mistral 7B v0.3 on A10G 24GB cost per million tokens?

$0.3094 per 1M output tokens at 1× A10G 24GB ($0.3600/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 A10G 24GB?

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 A10G 24GB the right choice for Mistral 7B v0.3?

It works. Compare $0.3094 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