Can Ministral 3 14B run on MacBook Air M4 24GB?

YES — Tight Fit

A82Great

Estimated from fit model

Ministral 3 14B needs ~15.4 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: TransformersCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 15.4 GB, 9.6 tok/s, Tight fit

15.4 GB required17.3 GB available

89% VRAM used

Fit status

Tight fit

Decode

9.6 tok/s

TTFT

20228 ms

Safe context

28K

Memory

15.4 GB / 17.3 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on MacBook Air M4 24GB
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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 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	9.6 tok/s	11034 ms	28K
Coding	A	Tight fit	10.1 tok/s	19136 ms	28K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	8.4 tok/s	33667 ms	28K
Reasoning	A	Tight fit	10.1 tok/s	22615 ms	28K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	8.4 tok/s	42084 ms	28K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A84
Q3_K_S	3	6.9 GB	Low	A85
NVFP4	4	7.8 GB	Medium	S86
Q4_K_M	4	8.5 GB	Medium	S86
Q5_K_M	5	10.1 GB	High	S86
Q6_KBest for your GPU	6	11.5 GB	High	S86
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Ministral 3 14B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-14B-Instruct-2512" \
  --hf-file "Ministral-3-14B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Air M4 24GB can run

Model	Params	Grade	Decode	Capabilities
Phi-4-reasoning-plus 14B	14.7B	S	9.4 tok/s
GPT-OSS 20B	21B	A	12.7 tok/s

Frequently asked questions

Can MacBook Air M4 24GB run Ministral 3 14B?

Yes, MacBook Air M4 24GB can run Ministral 3 14B with a A grade (Tight fit). Expected decode speed: 10.1 tok/s.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 14B?

The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 14B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Ministral 3 14B achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19136ms using Q4_K_M quantization.

Can MacBook Air M4 24GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on MacBook Air M4 24GB receives a A grade with 10.1 tok/s and 28K context.

What context window can Ministral 3 14B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Ministral 3 14B can safely use up to 28K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M4 24GB as fast as VRAM for Ministral 3 14B?

Not always. MacBook Air M4 24GB 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 Air M4 24GB See all hardware for Ministral 3 14B

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