Can Pixtral 12B run on RX 7900 XT 20GB?

YES — Runs Great

A78Great

○Estimated from fit model

Pixtral 12B needs ~12.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 12.7 GB, 70.5 tok/s, Runs well

12.7 GB required20.0 GB available

64% VRAM used

Fit status

Runs well

Decode

70.5 tok/s

TTFT

2746 ms

Safe context

64K

Memory

12.7 GB / 20.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsPixtral 12B on RX 7900 XT 20GB
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: 70.5 tok/s decode · 2.7s TTFT (warm) · 176 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	A	Runs well	70.5 tok/s	1498 ms	64K
Coding	A	Runs well	70.5 tok/s	2746 ms	64K
Agentic Coding	A	Runs well	70.5 tok/s	3995 ms	64K
Reasoning	A	Runs well	70.5 tok/s	3246 ms	64K
RAG	A	Runs well	70.5 tok/s	4993 ms	64K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A70
Q3_K_S	3	5.9 GB	Low	A71
NVFP4	4	6.7 GB	Medium	A72
Q4_K_M	4	7.3 GB	Medium	A72
Q5_K_M	5	8.6 GB	High	A73
Q6_K	6	9.8 GB	High	A74
Q8_0Best for your GPU	8	12.8 GB	Very High	A74
F16	16	24.6 GB	Maximum	F0

Get started

Copy-paste commands to run Pixtral 12B on your machine.

Run

ollama run pixtral

Your hardware

More models your RX 7900 XT 20GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	40.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	A	18.3 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	17.3 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	A	43.3 tok/s
👁 Mistral Magistral Small 2507	24B	S	35.2 tok/s

Frequently asked questions

See all results for RX 7900 XT 20GB See all hardware for Pixtral 12B

URL: https://willitrunai.com/can-run/pixtral-12b-on-rx-7900-xt-20gb