Can Mistral Small 3.2 24B Instruct 2506 run on Radeon Pro W7800 32GB?

YES — Runs Great

C52Usable

Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~21.6 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~23 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, 23.2 tok/s, Runs well

21.6 GB required32.0 GB available

68% VRAM used

Fit status

Runs well

Decode

23.2 tok/s

TTFT

8340 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 3.2 24B Instruct 2506 on Radeon Pro W7800 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: 23.2 tok/s decode · 8.3s TTFT (warm) · 58 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	23.2 tok/s	4549 ms	75K
Coding	C	Runs well	23.2 tok/s	8340 ms	75K
Agentic Coding	C	Runs well	23.2 tok/s	12131 ms	75K
Reasoning	C	Runs well	23.2 tok/s	9856 ms	75K
RAG	C	Runs well	23.2 tok/s	15164 ms	75K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on Radeon Pro W7800 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	C50
Q6_K	6	19.7 GB	High	C49
Q8_0Best for your GPU	8	25.7 GB	Very High	C49
F16	16	49.2 GB	Maximum	F0

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

Opções de upgrade

Hardware que roda bem Mistral Small 3.2 24B Instruct 2506

MacBook Pro M4 Max 48GBOpção econômica

48 GB Unified (+16)

Raises estimated decode speed by about 47%.34.2 tok/s decodificação

Raises estimated decode speed by about 47%.

~$2,499 MSRP

NVIDIA A100 40GBMelhor custo-benefício

40 GB VRAM (+8)1555 GB/s (+979)

Raises estimated decode speed by about 284%.89.2 tok/s decodificação

Raises estimated decode speed by about 284%.

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

~$10,000 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run Mistral Small 3.2 24B Instruct 2506?

Yes, Radeon Pro W7800 32GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Runs well). Expected decode speed: 23.2 tok/s.

How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?

Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 21.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B Instruct 2506?

The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B Instruct 2506 run at on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 23.2 tokens per second decode speed with a time-to-first-token of 8340ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on Radeon Pro W7800 32GB receives a C grade with 23.2 tok/s and 75K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Mistral Small 3.2 24B Instruct 2506 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 Pro W7800 32GB See all hardware for Mistral Small 3.2 24B Instruct 2506

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