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URL: https://willitrunai.com/can-run/ministral-8b-on-m3-ultra-256gb

⇱ Ministral 8B on Mac Studio M3 Ultra 256GB? YES

Can Ministral 8B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

B55Good
○Estimated from fit model

Ministral 8B needs ~35.6 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 35.6 GB, 112.0 tok/s, Runs well
35.6 GB required184.3 GB available
19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

131K

Memory

35.6 GB / 184.3 GB

Memory breakdown

Weights4.9 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom27.6 GB

See how fast it feels

See how fast it feelsMinistral 8B on Mac Studio M3 Ultra 256GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well112.0 tok/s943 ms131K
CodingBRuns well112.0 tok/s1729 ms131K
Agentic CodingBRuns well112.0 tok/s2514 ms131K
ReasoningBRuns well112.0 tok/s2043 ms131K
RAGBRuns well112.0 tok/s3143 ms131K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC47
Q3_K_S
3
3.9 GB
LowC47
NVFP4
4
4.5 GB
MediumC47
Q4_K_M
4
4.9 GB
MediumC47
Q5_K_M
5
5.8 GB
HighC47
Q6_K
6
6.6 GB
HighC47
Q8_0
8
8.6 GB
Very HighC47
F16Best for your GPU
16
16.4 GB
MaximumC47

Get started

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

Run

ollama run ministral

Frequently asked questions

See all results for Mac Studio M3 Ultra 256GBSee all hardware for Ministral 8B