Will It Run AI

Can Mistral Small 24B Instruct 2501 run on AMD Instinct MI250X 128GB?

YES — Runs Great

C47Usable
Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~31.2 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q4_K_M quantization, expect ~171 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 31.2 GB, 170.5 tok/s, Runs well
31.2 GB required128.0 GB available
24% VRAM used

Fit status

Runs well

Decode

170.5 tok/s

TTFT

1136 ms

Safe context

567K

Memory

31.2 GB / 128.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsMistral Small 24B Instruct 2501 on AMD Instinct MI250X 128GB
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: 170.5 tok/s decode · 1.1s TTFT (warm) · 426 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 well170.5 tok/s619 ms567K
CodingCRuns well170.5 tok/s1136 ms567K
Agentic CodingCRuns well170.5 tok/s1652 ms567K
ReasoningCRuns well170.5 tok/s1342 ms567K
RAGCRuns well170.5 tok/s2065 ms567K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD38
Q3_K_S
3
11.8 GB
LowD39
NVFP4
4
13.4 GB
MediumD39
Q4_K_M
4
14.6 GB
MediumD39
Q5_K_M
5
17.3 GB
HighD39
Q6_K
6
19.7 GB
HighD39
Q8_0
8
25.7 GB
Very HighD40
F16Best for your GPU
16
49.2 GB
MaximumC44

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 AMD Instinct MI250X 128GB run Mistral Small 24B Instruct 2501?

Yes, AMD Instinct MI250X 128GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 170.5 tok/s.

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

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 31.2 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 AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, Mistral Small 24B Instruct 2501 achieves approximately 170.5 tokens per second decode speed with a time-to-first-token of 1136ms using Q4_K_M quantization.

Can AMD Instinct MI250X 128GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on AMD Instinct MI250X 128GB receives a C grade with 170.5 tok/s and 567K context.

What context window can Mistral Small 24B Instruct 2501 use on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, Mistral Small 24B Instruct 2501 can safely use up to 567K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI250X 128GBSee all hardware for Mistral Small 24B Instruct 2501
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