Can Ministral 3 8B run on RX 9070 16GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Ministral 3 8B needs ~11.3 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~87 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: MediumStack: OptimizedBottleneck: 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) — 11.3 GB, 87.4 tok/s, Runs well

11.3 GB required16.0 GB available

71% VRAM used

Fit status

Runs well

Decode

87.4 tok/s

TTFT

2215 ms

Safe context

50K

Memory

11.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime2.6 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on RX 9070 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: 87.4 tok/s decode · 2.2s TTFT (warm) · 219 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	S	Runs well	87.4 tok/s	1208 ms	50K
Coding	S	Runs well	87.4 tok/s	2215 ms	50K
Agentic Coding	A	Tight fit	87.4 tok/s	3222 ms	50K
Reasoning	S	Runs well	87.4 tok/s	2618 ms	50K
RAG	A	Tight fit	87.4 tok/s	4027 ms	50K

Inference speed

Ministral 3 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ministral 3 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	82.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	15.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A78
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A79
Q4_K_M	4	4.9 GB	Medium	A79
Q5_K_M	5	5.8 GB	High	A80
Q6_K	6	6.6 GB	High	A81
Q8_0Best for your GPU	8	8.6 GB	Very High	A82
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Ministral 3 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
  --hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your RX 9070 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	77.7 tok/s
Qwen 3 14B	14B	S	50.2 tok/s
Ministral 3 14B	14B	S	49.9 tok/s

Frequently asked questions

Can RX 9070 16GB run Ministral 3 8B?

Yes, RX 9070 16GB can run Ministral 3 8B with a S grade (Runs well). Expected decode speed: 87.4 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 11.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 8B?

The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 8B run at on RX 9070 16GB?

On RX 9070 16GB, Ministral 3 8B achieves approximately 87.4 tokens per second decode speed with a time-to-first-token of 2215ms using Q4_K_M quantization.

Can RX 9070 16GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on RX 9070 16GB receives a S grade with 87.4 tok/s and 50K context.

What context window can Ministral 3 8B use on RX 9070 16GB?

On RX 9070 16GB, Ministral 3 8B can safely use up to 50K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RX 9070 16GB See all hardware for Ministral 3 8B

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