NVIDIA24 GB VRAM936 GB/s$0.25/hr

Mistral 7B v0.3 on RTX 3090 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 RTX 3090 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
42.02
tok/s per stream
Aggregate
504.2
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1377
1× RTX 3090 24GB
What changes as you add RTX 3090 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× RTX24 GB23.1 GB42.02504.26.72 s$0.1377✓
2× RTX48 GB23.1 GB71.4317143.38 s$0.0810✓
4× RTX96 GB23.1 GB142.968571.70 s$0.0405✓
8× RTX192 GB23.1 GB285.727428866.5 ms$0.0203✓
Concurrency vs context length
On 1× RTX 3090 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 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
AMD MI325X 288GB1×3232$0.3008
Frequently asked questions

Can Mistral 7B v0.3 run on RTX 3090 24GB?

Yes. Mistral 7B v0.3 at FP16 needs 23.1 GB including the KV cache, and RTX 3090 24GB has 24 GB — so it fits on a single GPU.

How fast is Mistral 7B v0.3 on RTX 3090 24GB?

42.02 tokens/sec per stream and 504.2 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 6.72 s, and inter-token latency is 23.8 ms.

What does Mistral 7B v0.3 on RTX 3090 24GB cost per million tokens?

$0.1377 per 1M output tokens at 1× RTX 3090 24GB ($0.2500/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 RTX 3090 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 RTX 3090 24GB the right choice for Mistral 7B v0.3?

It works. Compare $0.1377 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