Can Mistral Small 24B Instruct 2501 run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C52Usable

Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~21.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 25.8 tok/s, Runs well

21.6 GB required32.0 GB available

68% VRAM used

Fit status

Runs well

Decode

25.8 tok/s

TTFT

7506 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 24B Instruct 2501 on Radeon AI PRO R9700 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 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	25.8 tok/s	4094 ms	75K
Coding	C	Runs well	25.8 tok/s	7506 ms	75K
Agentic Coding	C	Runs well	25.8 tok/s	10918 ms	75K
Reasoning	C	Runs well	25.8 tok/s	8871 ms	75K
RAG	C	Runs well	25.8 tok/s	13647 ms	75K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on Radeon AI PRO R9700 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

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

Upgrade options

Hardware that runs Mistral Small 24B Instruct 2501 well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

Raises estimated decode speed by about 33%.34.2 tok/s decode

Raises estimated decode speed by about 33%.

~$2,499 MSRP

NVIDIA A100 40GBBest value

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 246%.89.2 tok/s decode

Raises estimated decode speed by about 246%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Mistral Small 24B Instruct 2501?

Yes, Radeon AI PRO R9700 32GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 25.8 tok/s.

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

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 21.6 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Mistral Small 24B Instruct 2501 achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7506ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on Radeon AI PRO R9700 32GB receives a C grade with 25.8 tok/s and 75K context.

What context window can Mistral Small 24B Instruct 2501 use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Mistral Small 24B Instruct 2501 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 Radeon AI PRO R9700 32GB See all hardware for Mistral Small 24B Instruct 2501

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