Can Ministral 8B run on RX 7900 XT 20GB?

YES — Runs Great

B62Good

○Estimated from fit model

Ministral 8B needs ~10.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~106 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) — 10.0 GB, 105.7 tok/s, Runs well

10.0 GB required20.0 GB available

50% VRAM used

Fit status

Runs well

Decode

105.7 tok/s

TTFT

1831 ms

Safe context

89K

Memory

10.0 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsMinistral 8B 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: 105.7 tok/s decode · 1.8s TTFT (warm) · 264 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	B	Runs well	105.7 tok/s	999 ms	89K
Coding	B	Runs well	105.7 tok/s	1831 ms	89K
Agentic Coding	B	Runs well	105.7 tok/s	2663 ms	89K
Reasoning	B	Runs well	105.7 tok/s	2164 ms	89K
RAG	B	Runs well	105.7 tok/s	3329 ms	89K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B55
Q3_K_S	3	3.9 GB	Low	B56
NVFP4	4	4.5 GB	Medium	B56
Q4_K_M	4	4.9 GB	Medium	B56
Q5_K_M	5	5.8 GB	High	B57
Q6_K	6	6.6 GB	High	B57
Q8_0Best for your GPU	8	8.6 GB	Very High	B59
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Ministral 8B on your machine.

Run

ollama run ministral

Frequently asked questions

See all results for RX 7900 XT 20GB See all hardware for Ministral 8B

URL: https://willitrunai.com/can-run/ministral-8b-on-rx-7900-xt-20gb