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URL: https://willitrunai.com/can-run/hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-instinct-mi210-64gb

⇱ Mistral Small 3.2 24B Instruct 2506 on AMD Instinct MI210 6…

Can Mistral Small 3.2 24B Instruct 2506 run on AMD Instinct MI210 64GB?

YES — Runs Great

C50Usable
○Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~24.8 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 24.8 GB, 76.1 tok/s, Runs well
24.8 GB required64.0 GB available
39% VRAM used

Fit status

Runs well

Decode

76.1 tok/s

TTFT

2545 ms

Safe context

239K

Memory

24.8 GB / 64.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on AMD Instinct MI210 64GB
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: 76.1 tok/s decode · 2.5s TTFT (warm) · 190 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 well76.1 tok/s1388 ms239K
CodingCRuns well76.1 tok/s2545 ms239K
Agentic CodingCRuns well76.1 tok/s3701 ms239K
ReasoningCRuns well76.1 tok/s3007 ms239K
RAGCRuns well76.1 tok/s4627 ms239K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC41
Q3_K_S
3
11.8 GB
LowC42
NVFP4
4
13.4 GB
MediumC42
Q4_K_M
4
14.6 GB
MediumC42
Q5_K_M
5
17.3 GB
HighC43
Q6_K
6
19.7 GB
HighC43
Q8_0
8
25.7 GB
Very HighC45
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 AMD Instinct MI210 64GBSee all hardware for Mistral Small 3.2 24B Instruct 2506