Can Magistral Small 2507 run on RTX 4090 24GB?

YES — Tight Fit

S94Excellent

Estimated from fit model

Magistral Small 2507 needs ~20.7 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~52 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, 56.3 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

56.3 tok/s

TTFT

3442 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 feelsMagistral Small 2507 on RTX 4090 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: 56.3 tok/s decode · 3.4s TTFT (warm) · 141 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	56.3 tok/s	1877 ms	38K
Coding	S	Tight fit	52.3 tok/s	3700 ms	38K
Agentic Coding	S	Runs with offload	56.3 tok/s	5006 ms	38K
Reasoning	S	Tight fit	56.3 tok/s	4067 ms	38K
RAG	S	Runs with offload	56.3 tok/s	6258 ms	38K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S90
Q3_K_S	3	11.8 GB	Low	S92
NVFP4	4	13.4 GB	Medium	S92
Q4_K_M	4	14.6 GB	Medium	S91
Q5_K_MBest for your GPU	5	17.3 GB	High	S91
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 Magistral Small 2507 on your machine.

Run

ollama run magistral

Your hardware

More models your RTX 4090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	115.8 tok/s
Qwen 3.5 27B	27B	S	50.2 tok/s
Qwen 3.6 27B	27B	S	50.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	119.8 tok/s
Qwen 3.5 35B A3B	35B	A	69.4 tok/s

Frequently asked questions

Can RTX 4090 24GB run Magistral Small 2507?

Yes, RTX 4090 24GB can run Magistral Small 2507 with a S grade (Tight fit). Expected decode speed: 52.3 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Magistral Small 2507?

The recommended quantization for Magistral Small 2507 is Q4_K_M, which balances quality and memory efficiency.

What speed will Magistral Small 2507 run at on RTX 4090 24GB?

On RTX 4090 24GB, Magistral Small 2507 achieves approximately 52.3 tokens per second decode speed with a time-to-first-token of 3700ms using Q4_K_M quantization.

Can RTX 4090 24GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on RTX 4090 24GB receives a S grade with 52.3 tok/s and 38K context.

What context window can Magistral Small 2507 use on RTX 4090 24GB?

On RTX 4090 24GB, Magistral Small 2507 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 4090 24GB See all hardware for Magistral Small 2507

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<iframe src="https://willitrunai.com/embed/magistral-small-2507-on-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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