Can GPT-OSS 20B run on Mac mini M2 24GB?

BARELY — Tight on Memory

A76Great

Estimated from fit model

GPT-OSS 20B needs ~18.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 18.7 GB, 10.9 tok/s, Very compromised (needs ~1 GB host RAM)

18.7 GB required17.3 GB available

108% VRAM needed

1.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

10.9 tok/s

TTFT

17834 ms

Safe context

Memory

18.7 GB / 17.3 GB

Offload

10%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on Mac mini M2 24GB
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: 10.9 tok/s decode · 17.8s TTFT (warm) · 27 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload (needs ~0.2 GB host RAM)	12.1 tok/s	8748 ms	6K
Coding	A	Very compromised (needs ~1 GB host RAM)	10.9 tok/s	17834 ms	6K
Agentic Coding	F	Too heavy	9.3 tok/s	30435 ms	6K
Reasoning	A	Very compromised (needs ~1 GB host RAM)	10.9 tok/s	21076 ms	6K
RAG	F	Too heavy	9.3 tok/s	38044 ms	6K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	S90
Q3_K_S	3	10.3 GB	Low	S89
NVFP4	4	11.8 GB	Medium	S89
Q4_K_MBest for your GPU	4	12.8 GB	Medium	S89
Q5_K_M	5	15.1 GB	High	F0
Q6_K	6	17.2 GB	High	F0
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 M2 24GB can run

Model	Params	Grade	Decode
Magistral Small 2507	24B	B	3.7 tok/s
Devstral Small 2 24B Instruct	24B	B	3.7 tok/s
Devstral Small 1.1	24B	B	3.7 tok/s

Frequently asked questions

Can Mac mini M2 24GB run GPT-OSS 20B?

Yes, Mac mini M2 24GB can run GPT-OSS 20B with a A grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 10.9 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 18.7 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 M2 24GB?

On Mac mini M2 24GB, GPT-OSS 20B achieves approximately 10.9 tokens per second decode speed with a time-to-first-token of 17834ms using Q4_K_M quantization.

Can Mac mini M2 24GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on Mac mini M2 24GB receives a A grade with 10.9 tok/s and 6K context.

What context window can GPT-OSS 20B use on Mac mini M2 24GB?

On Mac mini M2 24GB, GPT-OSS 20B can safely use up to 6K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 20B feels slow on Mac mini M2 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on Mac mini M2 24GB as fast as VRAM for GPT-OSS 20B?

Not always. Mac mini M2 24GB 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 M2 24GB See all hardware for GPT-OSS 20B

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