Can GPT-OSS 120B run on MacBook Pro M3 Max 128GB?

YES — With Offload

A84Great

Estimated from fit model

GPT-OSS 120B needs ~91.0 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 91.0 GB, 3.7 tok/s, Runs with offload

91.0 GB required92.2 GB available

99% VRAM used

Fit status

Runs with offload

Decode

3.7 tok/s

TTFT

52940 ms

Safe context

20K

Memory

91.0 GB / 92.2 GB

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsGPT-OSS 120B on MacBook Pro M3 Max 128GB
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: 3.7 tok/s decode · 52.9s TTFT (warm) · 9 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	3.7 tok/s	28876 ms	20K
Coding	A	Runs with offload	3.7 tok/s	52940 ms	20K
Agentic Coding	A	Runs with offload (needs ~2.8 GB host RAM)	3.4 tok/s	83156 ms	20K
Reasoning	A	Runs with offload	3.7 tok/s	62565 ms	20K
RAG	A	Runs with offload (needs ~2.8 GB host RAM)	3.4 tok/s	103946 ms	20K

Inference speed

GPT-OSS 120B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GPT-OSS 120B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~9 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	8.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.1	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	6.7	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	4.4	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.3	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	3.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.6	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.6	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.3	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How GPT-OSS 120B (117B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	45.6 GB	Low	S88
Q3_K_S	3	57.3 GB	Low	S88
NVFP4	4	65.5 GB	Medium	S88
Q4_K_MBest for your GPU	4	71.4 GB	Medium	S88
Q5_K_M	5	84.2 GB	High	F0
Q6_K	6	95.9 GB	High	F0
Q8_0	8	125.2 GB	Very High	F0
F16	16	239.8 GB	Maximum	F0

Get started

Copy-paste commands to run GPT-OSS 120B on your machine.

Run

ollama run gpt-oss:120b

Your hardware

More models your MacBook Pro M3 Max 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	3.3 tok/s
Qwen 3.5 122B A10B	122B	S	15 tok/s
Mistral Small 4 119B	119B	S	16 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 128GB run GPT-OSS 120B?

Yes, MacBook Pro M3 Max 128GB can run GPT-OSS 120B with a A grade (Runs with offload). Expected decode speed: 3.7 tok/s.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 91.0 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 120B?

The recommended quantization for GPT-OSS 120B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 120B run at on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, GPT-OSS 120B achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 52940ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on MacBook Pro M3 Max 128GB receives a A grade with 3.7 tok/s and 20K context.

What context window can GPT-OSS 120B use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, GPT-OSS 120B can safely use up to 20K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 120B feels slow on MacBook Pro M3 Max 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for GPT-OSS 120B?

Not always. MacBook Pro M3 Max 128GB 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 M3 Max 128GB See all hardware for GPT-OSS 120B

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