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URL: https://willitrunai.com/can-run/ministral-8b-on-rx-7900m-16gb

⇱ Ministral 8B on Radeon RX 7900M 16GB? YES

Can Ministral 8B run on Radeon RX 7900M 16GB?

YES — Runs Great

B64Good
○Estimated from fit model

Ministral 8B needs ~9.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 9.6 GB, 74.9 tok/s, Runs well
9.6 GB required16.0 GB available
60% VRAM used

Fit status

Runs well

Decode

74.9 tok/s

TTFT

2586 ms

Safe context

63K

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsMinistral 8B 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: 74.9 tok/s decode · 2.6s TTFT (warm) · 187 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well74.9 tok/s1411 ms63K
CodingBRuns well74.9 tok/s2586 ms63K
Agentic CodingBRuns well74.9 tok/s3762 ms63K
ReasoningBRuns well74.9 tok/s3056 ms63K
RAGBRuns well74.9 tok/s4702 ms63K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowB57
Q3_K_S
3
3.9 GB
LowB57
NVFP4
4
4.5 GB
MediumB58
Q4_K_M
4
4.9 GB
MediumB58
Q5_K_M
5
5.8 GB
HighB59
Q6_K
6
6.6 GB
HighB60
Q8_0Best for your GPU
8
8.6 GB
Very HighB61
F16
16
16.4 GB
MaximumF0

Get started

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

Run

ollama run ministral

Frequently asked questions

See all results for Radeon RX 7900M 16GBSee all hardware for Ministral 8B