Can Mistral Small 3.2 24B Instruct 2506 run on RX 7900 XTX 24GB?

YES — Tight Fit

C52Usable

Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~20.8 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 20.8 GB, 47.2 tok/s, Tight fit

20.8 GB required24.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

47.2 tok/s

TTFT

4101 ms

Safe context

34K

Memory

20.8 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on RX 7900 XTX 24GB
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: 47.2 tok/s decode · 4.1s TTFT (warm) · 118 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	47.2 tok/s	2237 ms	34K
Coding	C	Tight fit	47.2 tok/s	4101 ms	34K
Agentic Coding	C	Runs with offload	47.2 tok/s	5964 ms	34K
Reasoning	C	Tight fit	47.2 tok/s	4846 ms	34K
RAG	C	Runs with offload	47.2 tok/s	7456 ms	34K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C49
Q3_K_S	3	11.8 GB	Low	C50
NVFP4	4

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

Upgrade options

Hardware that runs Mistral Small 3.2 24B Instruct 2506 well

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.25.8 tok/s decode

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

~$1,899 MSRP

Radeon Pro W7800 32GBBest value

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.23.2 tok/s decode

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

~$2,499 MSRP

AMD Instinct MI60 32GBAMD upgrade

32 GB VRAM (+8)1024 GB/s (+64)

Adds memory headroom for longer context windows and future model growth.34.3 tok/s decode

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

~$8,999 MSRP

Frequently asked questions

Can RX 7900 XTX 24GB run Mistral Small 3.2 24B Instruct 2506?

Yes, RX 7900 XTX 24GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Tight fit). Expected decode speed: 47.2 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 20.8 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 RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 47.2 tokens per second decode speed with a time-to-first-token of 4101ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on RX 7900 XTX 24GB receives a C grade with 47.2 tok/s and 34K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XTX 24GB See all hardware for Mistral Small 3.2 24B Instruct 2506

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