Can Mistral Small 3.2 24B Instruct 2506 run on B100 192GB?

YES — Runs Great

C46Usable

Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~37.9 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~336 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) — 37.9 GB, 336.0 tok/s, Runs well

37.9 GB required192.0 GB available

20% VRAM used

Fit status

Runs well

Decode

336.0 tok/s

TTFT

576 ms

Safe context

893K

Memory

37.9 GB / 192.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on B100 192GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	336.0 tok/s	350 ms	893K
Coding	C	Runs well	336.0 tok/s	576 ms	893K
Agentic Coding	C	Runs well	336.0 tok/s	838 ms	893K
Reasoning	C	Runs well	336.0 tok/s	681 ms	893K
RAG	C	Runs well	336.0 tok/s	1048 ms	893K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	D37
Q3_K_S	3	11.8 GB	Low	D37
NVFP4	4	13.4 GB	Medium	D37
Q4_K_M	4	14.6 GB	Medium	D37
Q5_K_M	5	17.3 GB	High	D37
Q6_K	6	19.7 GB	High	D38
Q8_0	8	25.7 GB	Very High	D38
F16Best for your GPU	16	49.2 GB	Maximum	C41

Get started

Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.

Run

lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server start

Frequently asked questions

Can B100 192GB run Mistral Small 3.2 24B Instruct 2506?

Yes, B100 192GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Runs well). Expected decode speed: 336.0 tok/s.

How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?

Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 37.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B Instruct 2506?

The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B Instruct 2506 run at on B100 192GB?

On B100 192GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 336.0 tokens per second decode speed with a time-to-first-token of 576ms using Q4_K_M quantization.

Can B100 192GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on B100 192GB receives a C grade with 336.0 tok/s and 893K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on B100 192GB?

On B100 192GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 893K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for B100 192GB See all hardware for Mistral Small 3.2 24B Instruct 2506

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<iframe src="https://willitrunai.com/embed/hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-b100-192gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

336.0 tok/s

350 ms

893K

Coding

Runs well

336.0 tok/s

576 ms

893K

Agentic Coding

Runs well

336.0 tok/s

838 ms

893K

Reasoning

Runs well

336.0 tok/s

681 ms

893K

RAG

Runs well

336.0 tok/s

1048 ms

893K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	D37
Q3_K_S	3	11.8 GB	Low	D37
NVFP4	4	13.4 GB	Medium	D37
Q4_K_M	4	14.6 GB	Medium	D37
Q5_K_M	5	17.3 GB	High	D37
Q6_K	6	19.7 GB	High	D38
Q8_0	8	25.7 GB	Very High	D38
F16Best for your GPU	16	49.2 GB	Maximum	C41