Will It Run AI

Can Mistral Small 24B run on MacBook Air M3 24GB?

BARELY — Tight on Memory

B60Good
Estimated from fit model

Mistral Small 24B needs ~20.6 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
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) — 20.6 GB, 3.8 tok/s, Very compromised (needs ~2.3 GB host RAM)
20.6 GB required17.3 GB available
119% VRAM needed

3.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.3 GB host RAM)

Decode

3.8 tok/s

TTFT

50415 ms

Safe context

4K

Memory

20.6 GB / 17.3 GB

Offload

20%

Memory breakdown

Weights14.6 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsMistral Small 24B on MacBook Air M3 24GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 3.8 tok/s decode · 50.4s TTFT (warm) · 10 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised (needs ~1.6 GB host RAM)4.2 tok/s25402 ms4K
CodingBVery compromised (needs ~2.3 GB host RAM)3.8 tok/s50415 ms4K
Agentic CodingFToo heavy3.4 tok/s84051 ms4K
ReasoningBVery compromised (needs ~2.3 GB host RAM)3.8 tok/s59581 ms4K
RAGFToo heavy3.4 tok/s105063 ms4K

Quantization options

How Mistral Small 24B (24B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA83
Q3_K_SBest for your GPU
3
11.8 GB
LowA82
NVFP4
4
13.4 GB
MediumF0
Q4_K_M
4
14.6 GB
MediumF0
Q5_K_M
5
17.3 GB
HighF0
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

Get started

Copy-paste commands to run Mistral Small 24B on your machine.

Run

ollama run mistral-small

アップグレードオプション

Mistral Small 24Bを快適に動かすハードウェア

MacBook Pro M4 32GBコスパ重視
32 GB Unified (+8)120 GB/s (+20)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.9.5 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 150%.

〜$799 MSRP

Mac mini M4 32GBコスパ最良
32 GB Unified (+8)120 GB/s (+20)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.9.5 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 150%.

〜$1,099 MSRP

Mac mini M4 64GBAppleアップグレード
64 GB Unified (+40)120 GB/s (+20)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.9.5 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 150%.

〜$1,099 MSRP

NVIDIARTX 5090 32GB最大の飛躍
32 GB VRAM (+8)1792 GB/s (+1692)
S
Removes host-memory offload, which is usually the single biggest latency and throughput win.70.5 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1755%.

〜$1,999 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run Mistral Small 24B?

Yes, MacBook Air M3 24GB can run Mistral Small 24B with a B grade (Very compromised (needs ~2.3 GB host RAM)). Expected decode speed: 3.8 tok/s.

How much VRAM does Mistral Small 24B need?

Mistral Small 24B (24B parameters) requires approximately 20.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B?

The recommended quantization for Mistral Small 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B run at on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Mistral Small 24B achieves approximately 3.8 tokens per second decode speed with a time-to-first-token of 50415ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run Mistral Small 24B for coding?

For coding workloads, Mistral Small 24B on MacBook Air M3 24GB receives a B grade with 3.8 tok/s and 4K context.

What context window can Mistral Small 24B use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Mistral Small 24B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 24B feels slow on MacBook Air M3 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Air M3 24GB as fast as VRAM for Mistral Small 24B?

Not always. MacBook Air M3 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 M3 24GBSee all hardware for Mistral Small 24B
Embed this result▼

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

<iframe src="https://willitrunai.com/embed/mistral-small-24b-on-m3-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: