Can mistral small 3.1 24b instruct 2503 hf run on Radeon RX 7900M 16GB?

Q: What should I upgrade first if mistral small 3.1 24b instruct 2503 hf feels slow on Radeon RX 7900M 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

D38Poor

Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~18.8 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With NVFP4 quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

mistral small 3.1 24b instruct 2503 hf at Q4_K_M needs 20.0 GB — too much for Radeon RX 7900M 16GB (16.0 GB). Runs at NVFP4 (18.8 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.0 GB, exceeds 16.0 GB available

20.0 GB required16.0 GB available

125% VRAM needed

4.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.9 tok/s

TTFT

17698 ms

Safe context

Memory

20.0 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on Radeon RX 7900M 16GB
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: 10.9 tok/s decode · 17.7s TTFT (warm) · 27 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Very compromised (needs ~2 GB host RAM)	12.8 tok/s	8277 ms	4K
Coding	F	Too heavy	10.9 tok/s	17698 ms	4K
Agentic Coding	F	Too heavy	8.3 tok/s	33979 ms	4K
Reasoning	F	Too heavy	10.9 tok/s	20916 ms	4K
RAG	F	Too heavy	8.3 tok/s	42474 ms	4K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C51
Q3_K_SBest for your GPU	3	11.8 GB	Low	C51

Get started

Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server start

Upgrade options

Hardware that runs mistral small 3.1 24b instruct 2503 hf well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.23.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.25.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run mistral small 3.1 24b instruct 2503 hf?

Yes, Radeon RX 7900M 16GB can run mistral small 3.1 24b instruct 2503 hf at NVFP4 quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 20.0 GB which exceeds available memory, but at NVFP4 it needs only 18.8 GB. Expected decode speed: 14.3 tok/s.

How much VRAM does mistral small 3.1 24b instruct 2503 hf need?

mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 20.0 GB at Q4_K_M quantization. On Radeon RX 7900M 16GB, it fits at NVFP4 using 18.8 GB.

What is the best quantization for mistral small 3.1 24b instruct 2503 hf?

The recommended quantization is Q4_K_M, but on Radeon RX 7900M 16GB the best fitting quantization is NVFP4, which uses 18.8 GB.

What speed will mistral small 3.1 24b instruct 2503 hf run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13580ms using NVFP4 quantization.

Can Radeon RX 7900M 16GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on Radeon RX 7900M 16GB receives a F grade with 10.9 tok/s and 4K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on Radeon RX 7900M 16GB?

See all results for Radeon RX 7900M 16GB See all hardware for mistral small 3.1 24b instruct 2503 hf

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