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HomeCan I run it?Mistral Small 24BMistral Small 24B on an RTX 4060 Ti 16GB

No. Mistral Small 24B at Q4_K_M needs about 17.7 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a ~4k context.

Can I run Mistral Small 24B on an RTX 4060 Ti 16GB?

Mistral Small 24B at Q4_K_M needs about 17.7 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 288 GB/s memory bandwidth. Mistral’s 24B Small instruct model, a dense mid-size alternative to the 27B–32B class. These are estimates from our formula, not measured benchmarks.

Quant that fits
None of Q4, Q8 or FP16 needs 17.7 GB
Memory needed
17.7 GB at Q4_K_M, ~4k context
Usable VRAM
16.0 GB of 16 GB VRAM
Est. tokens/sec
n/a does not fit

Mistral-Small-3.2-24B-Instruct-2506 · GeForce RTX 4060 Ti 16GB · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

QuantNeed vs 16.0 GBVerdict
Q4_K_M17.7 GBQ4Won’t fit
Q8_030.3 GBQ8Won’t fit
FP1656.2 GBFP16Won’t fit

Estimated tokens per second

No speed estimate: Mistral Small 24B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

Context headroom

Even at a short ~4k context, the weights do not fit. Extending context would only increase the gap.

Context headroom for Mistral Small 24B on an RTX 4060 Ti 16GB at Q4_K_M
ContextKV cache (FP16)Total at Q4_K_MVerdict
4k0.7 GB17.7 GBWon’t fit
8k1.3 GB18.4 GBWon’t fit
16k2.7 GB19.8 GBWon’t fit
32k5.4 GB22.4 GBWon’t fit
64k10.7 GB27.8 GBWon’t fit
128k21.5 GB38.5 GBWon’t fit

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

Other hardware for Mistral Small 24B

Other models on an RTX 4060 Ti 16GB

Quick answers

Can I run Mistral Small 24B on an RTX 4060 Ti 16GB?
No. Mistral Small 24B at Q4_K_M needs about 17.7 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable.
How much VRAM does Mistral Small 24B need?
About 17.7 GB at Q4_K_M, 30.3 GB at Q8_0 and 56.2 GB at FP16, estimated for a ~4k context.
How fast is Mistral Small 24B on an RTX 4060 Ti 16GB?
We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.
What context length can I use?
The weights already do not fit, so longer context is not the limiting factor.

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