Can mistral small 3.1 24b instruct 2503 hf run on RTX 5090 32GB?

YES — Runs Great

B56Good

Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~21.9 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~82 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 21.9 GB, 82.0 tok/s, Runs well

21.9 GB required32.0 GB available

68% VRAM used

Fit status

Runs well

Decode

82.0 tok/s

TTFT

2361 ms

Safe context

74K

Memory

21.9 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on RTX 5090 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 82.0 tok/s decode · 2.4s TTFT (warm) · 205 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	82.0 tok/s	1288 ms	74K
Coding	B	Runs well	82.0 tok/s	2361 ms	74K
Agentic Coding	B	Runs well	82.0 tok/s	3434 ms	74K
Reasoning	B	Runs well	82.0 tok/s	2790 ms	74K
RAG	B	Runs well	82.0 tok/s	4292 ms	74K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C46
Q3_K_S	3	11.8 GB	Low	C47
NVFP4	4	13.4 GB	Medium	C48
Q4_K_M	4	14.6 GB	Medium	C48
Q5_K_M	5	17.3 GB	High	C49
Q6_K	6	19.7 GB	High	C49
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server start

Frequently asked questions

Can RTX 5090 32GB run mistral small 3.1 24b instruct 2503 hf?

Yes, RTX 5090 32GB can run mistral small 3.1 24b instruct 2503 hf with a B grade (Runs well). Expected decode speed: 82.0 tok/s.

How much VRAM does mistral small 3.1 24b instruct 2503 hf need?

mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 21.9 GB of memory with Q4_K_M quantization.

What is the best quantization for mistral small 3.1 24b instruct 2503 hf?

The recommended quantization for mistral small 3.1 24b instruct 2503 hf is Q4_K_M, which balances quality and memory efficiency.

What speed will mistral small 3.1 24b instruct 2503 hf run at on RTX 5090 32GB?

On RTX 5090 32GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2361ms using Q4_K_M quantization.

Can RTX 5090 32GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on RTX 5090 32GB receives a B grade with 82.0 tok/s and 74K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on RTX 5090 32GB?

On RTX 5090 32GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 74K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 32GB See all hardware for mistral small 3.1 24b instruct 2503 hf

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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-on-rtx-5090-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

82.0 tok/s

1288 ms

74K

Coding

Runs well

82.0 tok/s

2361 ms

74K

Agentic Coding

Runs well

82.0 tok/s

3434 ms

74K

Reasoning

Runs well

82.0 tok/s

2790 ms

74K

RAG

Runs well

82.0 tok/s

4292 ms

74K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C46
Q3_K_S	3	11.8 GB	Low	C47
NVFP4	4	13.4 GB	Medium	C48
Q4_K_M	4	14.6 GB	Medium	C48
Q5_K_M	5	17.3 GB	High	C49
Q6_K	6	19.7 GB	High	C49
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0