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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-on-rx-6700-xt-12gb

⇱ mistral small 3.1 24b instruct 2503 hf on RX 6700 XT 12GB? …

Can mistral small 3.1 24b instruct 2503 hf run on RX 6700 XT 12GB?

YES — With Q2_K

D31Poor
○Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~14.3 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q2_K quantization, expect ~9 tok/s.

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

mistral small 3.1 24b instruct 2503 hf at Q4_K_M needs 19.6 GB — too much for RX 6700 XT 12GB (12.0 GB). Runs at Q2_K (14.3 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.6 GB, exceeds 12.0 GB available
19.6 GB required12.0 GB available
163% VRAM needed

7.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.7 tok/s

TTFT

52888 ms

Safe context

4K

Memory

19.6 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom1.2 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 RX 6700 XT 12GB
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: 3.7 tok/s decode · 52.9s TTFT (warm) · 9 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 20% 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 1.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy4.3 tok/s24654 ms4K
CodingFToo heavy3.7 tok/s52888 ms4K
Agentic CodingFToo heavy2.8 tok/s102081 ms4K
ReasoningFToo heavy3.7 tok/s62504 ms4K
RAGFToo heavy2.8 tok/s127601 ms4K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowF0
Q3_K_S
3
11.8 GB
LowF0
NVFP4
4
13.4 GB
MediumF0
Q4_K_M
4
14.6 GB
MediumF0
Q5_K_M
5
17.3 GB
HighF0
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

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 (+8)800 GB/s (+416)
C
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 (+12)960 GB/s (+576)
C
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 (+20)640 GB/s (+256)
C
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

See all results for RX 6700 XT 12GBSee all hardware for mistral small 3.1 24b instruct 2503 hf