Will It Run AI

Can Qwen3-Coder 30B A3B Instruct run on MacBook Pro M1 Pro 32GB?

YES — With Offload

A82Great
Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~24.4 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
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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) — 24.4 GB, 17.7 tok/s, Runs with offload (needs ~1.1 GB host RAM)
24.4 GB required23.0 GB available
106% VRAM needed

1.4 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~1.1 GB host RAM)

Decode

17.7 tok/s

TTFT

10952 ms

Safe context

4K

Memory

24.4 GB / 23.0 GB

Offload

10%

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on MacBook Pro M1 Pro 32GB
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: 17.7 tok/s decode · 11.0s TTFT (warm) · 44 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 10% 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 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns with offload (needs ~0.5 GB host RAM)18.5 tok/s5694 ms4K
CodingARuns with offload (needs ~1.1 GB host RAM)17.7 tok/s10952 ms4K
Agentic CodingAVery compromised (needs ~2 GB host RAM)16.3 tok/s17284 ms4K
ReasoningARuns with offload (needs ~1.1 GB host RAM)17.7 tok/s12943 ms4K
RAGAVery compromised (needs ~2 GB host RAM)16.3 tok/s21605 ms4K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowS93
Q3_K_S
3
14.9 GB
LowS93
NVFP4Best for your GPU
4
17.1 GB
MediumS93
Q4_K_M
4
18.6 GB
MediumF0
Q5_K_M
5
22.0 GB
HighF0
Q6_K
6
25.0 GB
HighF0
Q8_0
8
32.6 GB
Very HighF0
F16
16
62.5 GB
MaximumF0

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Frequently asked questions

Can MacBook Pro M1 Pro 32GB run Qwen3-Coder 30B A3B Instruct?

Yes, MacBook Pro M1 Pro 32GB can run Qwen3-Coder 30B A3B Instruct with a A grade (Runs with offload (needs ~1.1 GB host RAM)). Expected decode speed: 17.7 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 24.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Qwen3-Coder 30B A3B Instruct achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10952ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 32GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on MacBook Pro M1 Pro 32GB receives a A grade with 17.7 tok/s and 4K context.

What context window can Qwen3-Coder 30B A3B Instruct use on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Qwen3-Coder 30B A3B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder 30B A3B Instruct feels slow on MacBook Pro M1 Pro 32GB?

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 Pro M1 Pro 32GB as fast as VRAM for Qwen3-Coder 30B A3B Instruct?

Not always. MacBook Pro M1 Pro 32GB 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 M1 Pro 32GBSee all hardware for Qwen3-Coder 30B A3B Instruct
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