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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf-on-radeon-pro-w7900-ds-48gb

⇱ Mistral Small 24B Instruct 2501 on Radeon PRO W7900 DS 48GB…

Can Mistral Small 24B Instruct 2501 run on Radeon PRO W7900 DS 48GB?

YES — Runs Great

C49Usable
○Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~23.2 GB VRAM. Radeon PRO W7900 DS 48GB has 48.0 GB. With Q4_K_M quantization, expect ~35 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) — 23.2 GB, 34.8 tok/s, Runs well
23.2 GB required48.0 GB available
48% VRAM used

Fit status

Runs well

Decode

34.8 tok/s

TTFT

5560 ms

Safe context

157K

Memory

23.2 GB / 48.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsMistral Small 24B Instruct 2501 on Radeon PRO W7900 DS 48GB
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: 34.8 tok/s decode · 5.6s TTFT (warm) · 87 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 well34.8 tok/s3033 ms157K
CodingCRuns well34.8 tok/s5560 ms157K
Agentic CodingCRuns well34.8 tok/s8087 ms157K
ReasoningCRuns well34.8 tok/s6571 ms157K
RAGCRuns well34.8 tok/s10109 ms157K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on Radeon PRO W7900 DS 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC43
Q3_K_S
3
11.8 GB
LowC44
NVFP4
4
13.4 GB
MediumC44
Q4_K_M
4
14.6 GB
MediumC44
Q5_K_M
5
17.3 GB
HighC45
Q6_K
6
19.7 GB
HighC46
Q8_0Best for your GPU
8
25.7 GB
Very HighC48
F16
16
49.2 GB
MaximumF0

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

AMD Instinct MI210 64GBBudget pick
64 GB VRAM (+16)1638 GB/s (+774)
C
Raises estimated decode speed by about 119%.76.1 tok/s decode

Raises estimated decode speed by about 119%.

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

~$10,000 MSRP

Frequently asked questions

See all results for Radeon PRO W7900 DS 48GBSee all hardware for Mistral Small 24B Instruct 2501