Will It Run AI

Can Qwen3-Coder 30B A3B Instruct run on MacBook Pro M4 Max 36GB?

YES — With Offload

S94Excellent
Estimated — low-sample bucket· few comparable runs

Qwen3-Coder 30B A3B Instruct needs ~24.9 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 24.9 GB, 39.1 tok/s, Runs with offload
24.9 GB required25.9 GB available
96% VRAM used

Fit status

Runs with offload

Decode

39.1 tok/s

TTFT

4957 ms

Safe context

28K

Memory

24.9 GB / 25.9 GB

Memory breakdown

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

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on MacBook Pro M4 Max 36GB
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: 39.1 tok/s decode · 5.0s TTFT (warm) · 98 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit39.1 tok/s2704 ms28K
CodingSRuns with offload39.1 tok/s4957 ms28K
Agentic CodingSRuns with offload (needs ~0.3 GB host RAM)37.7 tok/s7474 ms28K
ReasoningSRuns with offload39.1 tok/s5858 ms28K
RAGSRuns with offload (needs ~0.3 GB host RAM)37.7 tok/s9342 ms28K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowS93
Q3_K_S
3
14.9 GB
LowS93
NVFP4
4
17.1 GB
MediumS93
Q4_K_MBest for your GPU
4
18.6 GB
MediumS92
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 M4 Max 36GB run Qwen3-Coder 30B A3B Instruct?

Yes, MacBook Pro M4 Max 36GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 39.1 tok/s.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 24.9 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 M4 Max 36GB?

On MacBook Pro M4 Max 36GB, Qwen3-Coder 30B A3B Instruct achieves approximately 39.1 tokens per second decode speed with a time-to-first-token of 4957ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 36GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on MacBook Pro M4 Max 36GB receives a S grade with 39.1 tok/s and 28K context.

What context window can Qwen3-Coder 30B A3B Instruct use on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, Qwen3-Coder 30B A3B Instruct can safely use up to 28K 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 M4 Max 36GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for Qwen3-Coder 30B A3B Instruct?

Not always. MacBook Pro M4 Max 36GB 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 Max 36GBSee all hardware for Qwen3-Coder 30B A3B Instruct
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