Can Magistral Small 2507 run on RTX 5090 32GB?

YES — Runs Great

S98Excellent

Estimated from fit model

Magistral Small 2507 needs ~21.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 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) — 21.5 GB, 88.2 tok/s, Runs well

21.5 GB required32.0 GB available

67% VRAM used

Fit status

Runs well

Decode

88.2 tok/s

TTFT

2196 ms

Safe context

85K

Memory

21.5 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 on RTX 5090 32GB
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: 88.2 tok/s decode · 2.2s TTFT (warm) · 220 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	88.2 tok/s	1198 ms	85K
Coding	S	Runs well	88.2 tok/s	2196 ms	85K
Agentic Coding	S	Runs well	88.2 tok/s	3194 ms	85K
Reasoning	S	Runs well	88.2 tok/s	2595 ms	85K
RAG	S	Runs well	88.2 tok/s	3993 ms	85K

Quantization options

How Magistral Small 2507 (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	S87
Q3_K_S	3	11.8 GB	Low	S88
NVFP4	4

Get started

Copy-paste commands to run Magistral Small 2507 on your machine.

Run

ollama run magistral

Your hardware

More models your RTX 5090 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	181.6 tok/s
Qwen 3.5 27B	27B	S	78.7 tok/s

Frequently asked questions

Can RTX 5090 32GB run Magistral Small 2507?

Yes, RTX 5090 32GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 88.2 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 21.5 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 5090 32GB?

On RTX 5090 32GB, Magistral Small 2507 achieves approximately 88.2 tokens per second decode speed with a time-to-first-token of 2196ms using Q4_K_M quantization.

Can RTX 5090 32GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on RTX 5090 32GB receives a S grade with 88.2 tok/s and 85K context.

What context window can Magistral Small 2507 use on RTX 5090 32GB?

On RTX 5090 32GB, Magistral Small 2507 can safely use up to 85K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5090 32GB See all hardware for Magistral Small 2507

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