Can Magistral Small 2507 run on NVIDIA A100 80GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Magistral Small 2507 needs ~26.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~126 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) — 26.3 GB, 125.8 tok/s, Runs well

26.3 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

125.8 tok/s

TTFT

1539 ms

Safe context

131K

Memory

26.3 GB / 80.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 on NVIDIA A100 80GB
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: 125.8 tok/s decode · 1.5s TTFT (warm) · 314 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	125.8 tok/s	840 ms	131K
Coding	S	Runs well	125.8 tok/s	1539 ms	131K
Agentic Coding	S	Runs well	125.8 tok/s	2239 ms	131K
Reasoning	S	Runs well	125.8 tok/s	1819 ms	131K
RAG	S	Runs well	125.8 tok/s	2799 ms	131K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A82
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_M	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	A83
Q8_0	8	25.7 GB	Very High	A84
F16Best for your GPU	16	49.2 GB	Maximum	S89

Get started

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

Run

ollama run magistral

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	259 tok/s
Qwen 3.5 27B	27B	S	112.3 tok/s
Qwen 3.6 27B	27B	S	112.7 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Magistral Small 2507?

Yes, NVIDIA A100 80GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 125.8 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 26.3 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, Magistral Small 2507 achieves approximately 125.8 tokens per second decode speed with a time-to-first-token of 1539ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on NVIDIA A100 80GB receives a S grade with 125.8 tok/s and 131K context.

What context window can Magistral Small 2507 use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Magistral Small 2507 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Magistral Small 2507

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

125.8 tok/s

840 ms

131K

Coding

Runs well

125.8 tok/s

1539 ms

131K

Agentic Coding

Runs well

125.8 tok/s

2239 ms

131K

Reasoning

Runs well

125.8 tok/s

1819 ms

131K

RAG

Runs well

125.8 tok/s

2799 ms

131K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A82
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_M	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	A83
Q8_0	8	25.7 GB	Very High	A84
F16Best for your GPU	16	49.2 GB	Maximum	S89

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

17.6 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

259 tok/s

Qwen 3.5 27B

27B

112.3 tok/s

Qwen 3.6 27B

27B

112.7 tok/s

Qwen 3.5 122B A10B

122B

52.1 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Magistral Small 2507?

Yes, NVIDIA A100 80GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 125.8 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 26.3 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, Magistral Small 2507 achieves approximately 125.8 tokens per second decode speed with a time-to-first-token of 1539ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on NVIDIA A100 80GB receives a S grade with 125.8 tok/s and 131K context.

What context window can Magistral Small 2507 use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Magistral Small 2507 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.