willitrun·ai

Can Ministral 8B run on Mac mini M4 64GB?

YES — Runs Great

C53Usable
Estimated — low-sample bucket· few comparable runs

Ministral 8B needs ~14.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 14.9 GB, 17.5 tok/s, Runs well
14.9 GB required46.1 GB available
32% VRAM used

Fit status

Runs well

Decode

17.5 tok/s

TTFT

11056 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 mini M4 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: 17.5 tok/s decode · 11.1s TTFT (warm) · 44 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
ChatCRuns well17.5 tok/s6031 ms131K
CodingCRuns well17.5 tok/s11056 ms131K
Agentic CodingCRuns well17.5 tok/s16082 ms131K
ReasoningCRuns well17.5 tok/s13067 ms131K
RAGCRuns well17.5 tok/s20103 ms131K

Inference speed

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

Estimated decode speed (tokens/sec) for Ministral 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M112.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M112.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M112.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M112.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M112.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M112.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M102.2Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M96.9Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M83.3Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M82.6Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M82.6Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M52.9Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M52.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M48.5Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M42.6Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M25.1Heavy offload

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 8B (8B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC51
Q3_K_S
3
3.9 GB
LowC51
NVFP4
4
4.5 GB
MediumC51
Q4_K_M
4
4.9 GB
MediumC51
Q5_K_M
5
5.8 GB
HighC51
Q6_K
6
6.6 GB
HighC52
Q8_0
8
8.6 GB
Very HighC52
F16Best for your GPU
16
16.4 GB
MaximumC55

Get started

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

Run

ollama run ministral

Opciones de mejora

Hardware que ejecuta bien Ministral 8B

MacBook Pro M4 Max 96GBOpción económica
96 GB Unified (+32)546 GB/s (+426)
B
Sube la velocidad estimada de decodificación alrededor de un 372%.82.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 372%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

MacBook Pro M3 Max 128GBMejor relación calidad-precio
128 GB Unified (+64)400 GB/s (+280)
C
Sube la velocidad estimada de decodificación alrededor de un 202%.52.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 202%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejora Apple
96 GB Unified (+32)819 GB/s (+699)
B
Sube la velocidad estimada de decodificación alrededor de un 540%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 540%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Ministral 8B?

Yes, Mac mini M4 64GB can run Ministral 8B with a C grade (Runs well). Expected decode speed: 17.5 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 mini M4 64GB?

On Mac mini M4 64GB, Ministral 8B achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11056ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on Mac mini M4 64GB receives a C grade with 17.5 tok/s and 131K context.

What context window can Ministral 8B use on Mac mini M4 64GB?

On Mac mini M4 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 mini M4 64GB as fast as VRAM for Ministral 8B?

Not always. Mac mini M4 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 mini M4 64GBSee all hardware for Ministral 8B
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