Can GPT-OSS 20B run on Mac mini M4 32GB?

YES — Tight Fit

S87Excellent

Estimated from fit model

GPT-OSS 20B needs ~19.6 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: 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) — 19.6 GB, 16.6 tok/s, Tight fit

19.6 GB required23.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

16.6 tok/s

TTFT

11672 ms

Safe context

38K

Memory

19.6 GB / 23.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on Mac mini M4 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: 16.6 tok/s decode · 11.7s TTFT (warm) · 42 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	S	Runs well	16.6 tok/s	6367 ms	38K
Coding	S	Tight fit	15.4 tok/s	12548 ms	38K
Agentic Coding	S	Runs with offload	16.6 tok/s	16978 ms	38K
Reasoning	S	Tight fit	16.6 tok/s	13795 ms	38K
RAG	S	Runs with offload	16.6 tok/s	21222 ms	38K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	S87
Q3_K_S	3	10.3 GB	Low	S88
NVFP4	4

Get started

Copy-paste commands to run GPT-OSS 20B on your machine.

Run

ollama run gpt-oss

Your hardware

More models your Mac mini M4 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
Qwen 3.5 27B	27B	S	8.6 tok/s

Frequently asked questions

Can Mac mini M4 32GB run GPT-OSS 20B?

Yes, Mac mini M4 32GB can run GPT-OSS 20B with a S grade (Tight fit). Expected decode speed: 15.4 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 19.6 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 20B?

The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 20B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, GPT-OSS 20B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12548ms using Q4_K_M quantization.

Can Mac mini M4 32GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on Mac mini M4 32GB receives a S grade with 15.4 tok/s and 38K context.

What context window can GPT-OSS 20B use on Mac mini M4 32GB?

On Mac mini M4 32GB, GPT-OSS 20B can safely use up to 38K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 32GB as fast as VRAM for GPT-OSS 20B?

Not always. Mac mini M4 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 Mac mini M4 32GB See all hardware for GPT-OSS 20B

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