Will It Run AI

Can Mistral Small 24B Instruct 2501 run on NVIDIA B200 180GB?

YES — Runs Great

C46Usable
Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~36.7 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~336 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 36.7 GB, 336.0 tok/s, Runs well
36.7 GB required180.0 GB available
20% VRAM used

Fit status

Runs well

Decode

336.0 tok/s

TTFT

576 ms

Safe context

831K

Memory

36.7 GB / 180.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom18.0 GB

See how fast it feels

See how fast it feelsMistral Small 24B Instruct 2501 on NVIDIA B200 180GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 336.0 tok/s decode · 576ms TTFT (warm) · 840 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well336.0 tok/s350 ms831K
CodingCRuns well336.0 tok/s576 ms831K
Agentic CodingCRuns well336.0 tok/s838 ms831K
ReasoningCRuns well336.0 tok/s681 ms831K
RAGCRuns well336.0 tok/s1048 ms831K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD37
Q3_K_S
3
11.8 GB
LowD37
NVFP4
4
13.4 GB
MediumD37
Q4_K_M
4
14.6 GB
MediumD37
Q5_K_M
5
17.3 GB
HighD38
Q6_K
6
19.7 GB
HighD38
Q8_0
8
25.7 GB
Very HighD38
F16Best for your GPU
16
49.2 GB
MaximumC41

Get started

Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server start

Frequently asked questions

Can NVIDIA B200 180GB run Mistral Small 24B Instruct 2501?

Yes, NVIDIA B200 180GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 336.0 tok/s.

How much VRAM does Mistral Small 24B Instruct 2501 need?

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 36.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B Instruct 2501?

The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B Instruct 2501 run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Mistral Small 24B Instruct 2501 achieves approximately 336.0 tokens per second decode speed with a time-to-first-token of 576ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on NVIDIA B200 180GB receives a C grade with 336.0 tok/s and 831K context.

What context window can Mistral Small 24B Instruct 2501 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Mistral Small 24B Instruct 2501 can safely use up to 831K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GBSee all hardware for Mistral Small 24B Instruct 2501
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