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

Inference speed

Magistral Small 2507 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Magistral Small 2507 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/magistral-small-2507-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: