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URL: https://willitrunai.com/can-run/hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-a100-80gb

⇱ Mistral Small 3.2 24B Instruct 2506 on NVIDIA A100 80GB? YES

Can Mistral Small 3.2 24B Instruct 2506 run on NVIDIA A100 80GB?

YES — Runs Great

C49Usable
○Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~26.7 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~117 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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) — 26.7 GB, 117.0 tok/s, Runs well
26.7 GB required80.0 GB available
33% VRAM used

Fit status

Runs well

Decode

117.0 tok/s

TTFT

1655 ms

Safe context

319K

Memory

26.7 GB / 80.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom8.0 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on NVIDIA A100 80GB
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: 117.0 tok/s decode · 1.7s TTFT (warm) · 293 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 well117.0 tok/s903 ms319K
CodingCRuns well117.0 tok/s1655 ms319K
Agentic CodingCRuns well117.0 tok/s2407 ms319K
ReasoningCRuns well117.0 tok/s1956 ms319K
RAGCRuns well117.0 tok/s3009 ms319K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC40
Q3_K_S
3
11.8 GB
LowC41
NVFP4
4
13.4 GB
MediumC41
Q4_K_M
4
14.6 GB
MediumC41
Q5_K_M
5
17.3 GB
HighC41
Q6_K
6
19.7 GB
HighC42
Q8_0
8
25.7 GB
Very HighC43
F16Best for your GPU
16
49.2 GB
MaximumC48

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

See all results for NVIDIA A100 80GBSee all hardware for Mistral Small 3.2 24B Instruct 2506