Can Ministral 8B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

B58Good

Estimated from fit model

Ministral 8B needs ~14.9 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~95 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) — 14.9 GB, 102.2 tok/s, Runs well

14.9 GB required46.1 GB available

32% VRAM used

Fit status

Runs well

Decode

102.2 tok/s

TTFT

1894 ms

Safe context

131K

Memory

14.9 GB / 46.1 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsMinistral 8B on Mac Studio M2 Ultra 64GB
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	B	Runs well	102.2 tok/s	1033 ms	131K
Coding	B	Runs well	95.1 tok/s	2036 ms	131K
Agentic Coding	B	Runs well	102.2 tok/s	2755 ms	131K
Reasoning	B	Runs well	102.2 tok/s	2238 ms	131K
RAG	B	Runs well	102.2 tok/s	3444 ms	131K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C51
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C51
Q5_K_M	5	5.8 GB	High	C51
Q6_K	6	6.6 GB	High	C52
Q8_0	8	8.6 GB	Very High	C52
F16Best for your GPU	16	16.4 GB	Maximum	C55

Get started

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

Run

ollama run ministral

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Ministral 8B?

Yes, Mac Studio M2 Ultra 64GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 95.1 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 8B?

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

What speed will Ministral 8B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Ministral 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 64GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on Mac Studio M2 Ultra 64GB receives a B grade with 95.1 tok/s and 131K context.

What context window can Ministral 8B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Ministral 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Ministral 8B?

Not always. Mac Studio M2 Ultra 64GB 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 64GB See all hardware for Ministral 8B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/ministral-8b-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: