Will It Run AI

Can Ministral 8B run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

B57Good
Estimated — low-sample bucket· few comparable runs

Ministral 8B needs ~13.2 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) 13.2 GB, 42.6 tok/s, Runs well
13.2 GB required34.6 GB available
38% VRAM used

Fit status

Runs well

Decode

42.6 tok/s

TTFT

4544 ms

Safe context

131K

Memory

13.2 GB / 34.6 GB

Memory breakdown

Weights4.9 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom5.2 GB

See how fast it feels

See how fast it feelsMinistral 8B on MacBook Pro M4 Pro 48GB
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: 42.6 tok/s decode · 4.5s TTFT (warm) · 107 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 well42.6 tok/s2479 ms131K
CodingBRuns well42.6 tok/s4544 ms131K
Agentic CodingBRuns well42.6 tok/s6610 ms131K
ReasoningBRuns well42.6 tok/s5371 ms131K
RAGBRuns well42.6 tok/s8263 ms131K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC52
Q3_K_S
3
3.9 GB
LowC52
NVFP4
4
4.5 GB
MediumC53
Q4_K_M
4
4.9 GB
MediumC53
Q5_K_M
5
5.8 GB
HighC53
Q6_K
6
6.6 GB
HighC53
Q8_0
8
8.6 GB
Very HighC54
F16Best for your GPU
16
16.4 GB
MaximumB57

Get started

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

Run

ollama run ministral

Opções de upgrade

Hardware que roda bem Ministral 8B

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run Ministral 8B?

Yes, MacBook Pro M4 Pro 48GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 42.6 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 13.2 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 MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Ministral 8B achieves approximately 42.6 tokens per second decode speed with a time-to-first-token of 4544ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on MacBook Pro M4 Pro 48GB receives a B grade with 42.6 tok/s and 131K context.

What context window can Ministral 8B use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, 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 MacBook Pro M4 Pro 48GB as fast as VRAM for Ministral 8B?

Not always. MacBook Pro M4 Pro 48GB 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 MacBook Pro M4 Pro 48GBSee all hardware for Ministral 8B
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