Can Magistral Small 2507 run on RTX 4000 Ada 20GB?

YES — With Offload

S91Excellent

Estimated from fit model

Magistral Small 2507 needs ~20.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 20.0 GB, 20.6 tok/s, Runs with offload

20.0 GB required20.0 GB available

100% VRAM used

Fit status

Runs with offload

Decode

20.6 tok/s

TTFT

9389 ms

Safe context

16K

Memory

20.0 GB / 20.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 on RTX 4000 Ada 20GB
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: 20.6 tok/s decode · 9.4s TTFT (warm) · 52 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	20.6 tok/s	5122 ms	16K
Coding	S	Runs with offload	20.6 tok/s	9389 ms	16K
Agentic Coding	A	Very compromised (needs ~1.6 GB host RAM)	12.2 tok/s	23165 ms	16K
Reasoning	S	Runs with offload	20.6 tok/s	11097 ms	16K
RAG	A	Very compromised (needs ~1.6 GB host RAM)	12.2 tok/s	28957 ms	16K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S92
Q3_K_S	3	11.8 GB	Low	S92
NVFP4	4	13.4 GB	Medium	S92
Q4_K_MBest for your GPU	4	14.6 GB	Medium	S91
Q5_K_M	5	17.3 GB	High	F0
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 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.8 tok/s
Qwen 3.5 27B	27B	A	10.7 tok/s
Qwen 3.6 27B	27B	S	10.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	25.3 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run Magistral Small 2507?

Yes, RTX 4000 Ada 20GB can run Magistral Small 2507 with a S grade (Runs with offload). Expected decode speed: 20.6 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 20.0 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Magistral Small 2507 achieves approximately 20.6 tokens per second decode speed with a time-to-first-token of 9389ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on RTX 4000 Ada 20GB receives a S grade with 20.6 tok/s and 16K context.

What context window can Magistral Small 2507 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Magistral Small 2507 can safely use up to 16K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Magistral Small 2507 feels slow on RTX 4000 Ada 20GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4000 Ada 20GB See all hardware for Magistral Small 2507

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