Can Ministral 3 8B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A78Great

Estimated from fit model

Ministral 3 8B needs ~22.7 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: TransformersCapacity: 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) — 22.7 GB, 102.2 tok/s, Runs well

22.7 GB required92.2 GB available

25% VRAM used

Fit status

Runs well

Decode

102.2 tok/s

TTFT

1894 ms

Safe context

262K

Memory

22.7 GB / 92.2 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.8 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on Mac Studio M2 Ultra 128GB
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: 102.2 tok/s decode · 1.9s TTFT (warm) · 256 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	102.2 tok/s	1033 ms	262K
Coding	A	Runs well	95.1 tok/s	2036 ms	262K
Agentic Coding	A	Runs well	102.2 tok/s	2755 ms	262K
Reasoning	A	Runs well	102.2 tok/s	2238 ms	262K
RAG	A	Runs well	102.2 tok/s	3444 ms	262K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B70
Q3_K_S	3	3.9 GB	Low	B70
NVFP4	4	4.5 GB	Medium	B70
Q4_K_M	4	4.9 GB	Medium	B70
Q5_K_M	5	5.8 GB	High	B70
Q6_K	6	6.6 GB	High	B70
Q8_0	8	8.6 GB	Very High	B70
F16Best for your GPU	16	16.4 GB	Maximum	A71

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 Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	5.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s
Qwen 3.6 27B	27B	S	30.5 tok/s
Qwen 3.5 122B A10B	122B	S	16.9 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Ministral 3 8B?

Yes, Mac Studio M2 Ultra 128GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 95.1 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 22.7 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 Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Ministral 3 8B achieves approximately 95.1 tokens per second decode speed with a time-to-first-token of 2036ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on Mac Studio M2 Ultra 128GB receives a A grade with 95.1 tok/s and 262K context.

What context window can Ministral 3 8B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Ministral 3 8B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Ministral 3 8B?

Not always. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M2 Ultra 128GB See all hardware for Ministral 3 8B

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