Can mistral small 3.1 24b instruct 2503 hf run on AMD Instinct MI100 32GB?

YES — Runs Great

C55Usable

Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~21.6 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 21.6 GB, 54.5 tok/s, Runs well

21.6 GB required32.0 GB available

68% VRAM used

Fit status

Runs well

Decode

54.5 tok/s

TTFT

3551 ms

Safe context

75K

Memory

21.6 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on AMD Instinct MI100 32GB
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: 54.5 tok/s decode · 3.6s TTFT (warm) · 136 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	54.5 tok/s	1937 ms	75K
Coding	C	Runs well	54.5 tok/s	3551 ms	75K
Agentic Coding	B	Runs well	54.5 tok/s	5165 ms	75K
Reasoning	C	Runs well	54.5 tok/s	4196 ms	75K
RAG	B	Runs well	54.5 tok/s	6456 ms	75K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C46
Q3_K_S	3	11.8 GB	Low	C47
NVFP4	4	13.4 GB	Medium	C48
Q4_K_M	4	14.6 GB	Medium	C48
Q5_K_M	5	17.3 GB	High	C49
Q6_K	6	19.7 GB	High	C49
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI100 32GB run mistral small 3.1 24b instruct 2503 hf?

Yes, AMD Instinct MI100 32GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 54.5 tok/s.

How much VRAM does mistral small 3.1 24b instruct 2503 hf need?

mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 21.6 GB of memory with Q4_K_M quantization.

What is the best quantization for mistral small 3.1 24b instruct 2503 hf?

The recommended quantization for mistral small 3.1 24b instruct 2503 hf is Q4_K_M, which balances quality and memory efficiency.

What speed will mistral small 3.1 24b instruct 2503 hf run at on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 54.5 tokens per second decode speed with a time-to-first-token of 3551ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on AMD Instinct MI100 32GB receives a C grade with 54.5 tok/s and 75K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 75K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI100 32GB See all hardware for mistral small 3.1 24b instruct 2503 hf

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

54.5 tok/s

1937 ms

75K

Coding

Runs well

54.5 tok/s

3551 ms

75K

Agentic Coding

Runs well

54.5 tok/s

5165 ms

75K

Reasoning

Runs well

54.5 tok/s

4196 ms

75K

RAG

Runs well

54.5 tok/s

6456 ms

75K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C46
Q3_K_S	3	11.8 GB	Low	C47
NVFP4	4	13.4 GB	Medium	C48
Q4_K_M	4	14.6 GB	Medium	C48
Q5_K_M	5	17.3 GB	High	C49
Q6_K	6	19.7 GB	High	C49
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0