Will It Run AI

Can Qwen3-Coder 30B A3B Instruct run on MacBook Pro M4 Max 96GB?

YES — Runs Great

S93Excellent
Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~31.3 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 31.3 GB, 52.0 tok/s, Runs well
31.3 GB required69.1 GB available
45% VRAM used

Fit status

Runs well

Decode

52.0 tok/s

TTFT

3722 ms

Safe context

256K

Memory

31.3 GB / 69.1 GB

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on MacBook Pro M4 Max 96GB
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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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
ChatSRuns well52.0 tok/s2030 ms256K
CodingSRuns well52.0 tok/s3722 ms256K
Agentic CodingSRuns well52.0 tok/s5414 ms256K
ReasoningSRuns well52.0 tok/s4399 ms256K
RAGSRuns well52.0 tok/s6767 ms256K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowA84
Q3_K_S
3
14.9 GB
LowA85
NVFP4
4
17.1 GB
MediumS85
Q4_K_M
4
18.6 GB
MediumS86
Q5_K_M
5
22.0 GB
HighS86
Q6_K
6
25.0 GB
HighS87
Q8_0Best for your GPU
8
32.6 GB
Very HighS89
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 96GB run Qwen3-Coder 30B A3B Instruct?

Yes, MacBook Pro M4 Max 96GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 52.0 tok/s.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 31.3 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 96GB?

On MacBook Pro M4 Max 96GB, Qwen3-Coder 30B A3B Instruct achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on MacBook Pro M4 Max 96GB receives a S grade with 52.0 tok/s and 256K context.

What context window can Qwen3-Coder 30B A3B Instruct use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Qwen3-Coder 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Qwen3-Coder 30B A3B Instruct?

Not always. MacBook Pro M4 Max 96GB 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 96GBSee all hardware for Qwen3-Coder 30B A3B Instruct
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