Can GPT-OSS 20B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

S86Excellent

○Estimated from fit model

GPT-OSS 20B needs ~26.5 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 26.5 GB, 44.5 tok/s, Runs well

26.5 GB required69.1 GB available

38% VRAM used

Fit status

Runs well

Decode

44.5 tok/s

TTFT

4347 ms

Safe context

128K

Memory

26.5 GB / 69.1 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on MacBook Pro M2 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: 44.5 tok/s decode · 4.3s TTFT (warm) · 111 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	44.5 tok/s	2371 ms	128K
Coding	S	Runs well	44.5 tok/s	4347 ms	128K
Agentic Coding	S	Runs well	44.5 tok/s	6323 ms	128K
Reasoning	S	Runs well	44.5 tok/s	5137 ms	128K
RAG	S	Runs well	44.5 tok/s	7903 ms	128K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	A79
Q3_K_S	3	10.3 GB	Low	A79
NVFP4	4	11.8 GB	Medium	A80
Q4_K_M	4	12.8 GB	Medium	A80
Q5_K_M	5	15.1 GB	High	A80
Q6_K	6	17.2 GB	High	A81
Q8_0	8	22.5 GB	Very High	A82
F16Best for your GPU	16	43.1 GB	Maximum	S86

Get started

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

Run

ollama run gpt-oss

Your hardware

More models your MacBook Pro M2 Max 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	35.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	15.2 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	11.6 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	32.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	36.3 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Max 96GB See all hardware for GPT-OSS 20B

URL: https://willitrunai.com/can-run/gpt-oss-20b-on-m2-max-96gb

⇱ GPT-OSS 20B on MacBook Pro M2 Max 96GB? YES