Can Mistral Small 3.2 24B run on RTX 5090 Laptop 24GB?

YES — Tight Fit

S86Excellent

Estimated from fit model

Mistral Small 3.2 24B needs ~20.7 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 20.7 GB, 55.3 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

55.3 tok/s

TTFT

3503 ms

Safe context

38K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B on RTX 5090 Laptop 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 55.3 tok/s decode · 3.5s TTFT (warm) · 138 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	S	Runs well	55.3 tok/s	1911 ms	38K
Coding	S	Tight fit	55.3 tok/s	3503 ms	38K
Agentic Coding	S	Runs with offload	55.3 tok/s	5095 ms	38K
Reasoning	S	Tight fit	55.3 tok/s	4140 ms	38K
RAG	S	Runs with offload	55.3 tok/s	6369 ms	38K

Quantization options

How Mistral Small 3.2 24B (24B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A83
Q3_K_S	3	11.8 GB	Low	A84
NVFP4	4	13.4 GB	Medium	A84
Q4_K_M	4	14.6 GB	Medium	A84
Q5_K_MBest for your GPU	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run Mistral Small 3.2 24B on your machine.

Run

ollama run mistral-small3.2

Your hardware

More models your RTX 5090 Laptop 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	113.8 tok/s
Qwen 3.5 27B	27B	S	49.4 tok/s
Qwen 3.6 27B	27B	S	49.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	117.7 tok/s
Qwen 3.5 35B A3B	35B	A	63.8 tok/s

Frequently asked questions

Can RTX 5090 Laptop 24GB run Mistral Small 3.2 24B?

Yes, RTX 5090 Laptop 24GB can run Mistral Small 3.2 24B with a S grade (Tight fit). Expected decode speed: 55.3 tok/s.

How much VRAM does Mistral Small 3.2 24B need?

Mistral Small 3.2 24B (24B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B?

The recommended quantization for Mistral Small 3.2 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B run at on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Mistral Small 3.2 24B achieves approximately 55.3 tokens per second decode speed with a time-to-first-token of 3503ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run Mistral Small 3.2 24B for coding?

For coding workloads, Mistral Small 3.2 24B on RTX 5090 Laptop 24GB receives a S grade with 55.3 tok/s and 38K context.

What context window can Mistral Small 3.2 24B use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Mistral Small 3.2 24B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5090 Laptop 24GB See all hardware for Mistral Small 3.2 24B

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