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 ~17 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	16.6 tok/s	11672 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	11.8 GB	Medium	S89
Q4_K_M	4	12.8 GB	Medium	S89
Q5_K_M	5	15.1 GB	High	S88
Q6_KBest for your GPU	6	17.2 GB	High	S88
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

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
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	8.6 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	7.1 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	12.4 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	10.2 tok/s

Frequently asked questions

See all results for Mac mini M4 32GB See all hardware for GPT-OSS 20B

URL: https://willitrunai.com/can-run/gpt-oss-20b-on-m4-mini-32gb

⇱ GPT-OSS 20B on Mac mini M4 32GB? TIGHT FIT