Can GPT-OSS 120B run on Mac Studio M3 Ultra 96GB?

YES — With NVFP4

A70Great

Estimated from fit model

GPT-OSS 120B needs ~81.7 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With NVFP4 quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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.

GPT-OSS 120B at Q4_K_M needs 87.5 GB — too much for Mac Studio M3 Ultra 96GB (69.1 GB). Runs at NVFP4 (81.7 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 87.5 GB, exceeds 69.1 GB available

87.5 GB required69.1 GB available

127% VRAM needed

18.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.0 tok/s

TTFT

32006 ms

Safe context

Memory

87.5 GB / 69.1 GB

Offload

20%

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 120B on Mac Studio M3 Ultra 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: 6.0 tok/s decode · 32.0s TTFT (warm) · 15 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 20% 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 10.1 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	F	Too heavy	6.3 tok/s	16865 ms	4K
Coding	F	Too heavy	6.0 tok/s	32006 ms	4K
Agentic Coding	F	Too heavy	5.7 tok/s	49674 ms	4K
Reasoning	F	Too heavy	6.0 tok/s	37825 ms	4K
RAG	F	Too heavy	5.7 tok/s	62092 ms	4K

Quantization options

How GPT-OSS 120B (117B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	45.6 GB	Low	S88
Q3_K_S	3	57.3 GB	Low	F0
NVFP4	4	65.5 GB	Medium	F0
Q4_K_M	4	71.4 GB	Medium	F0
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

Upgrade-Optionen

Hardware, die GPT-OSS 120B gut ausführt

MacBook Pro M3 Max 128GBGünstige Wahl

128 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.3.7 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $2,499 MSRP

Mac Studio M2 Ultra 128GBBestes Preis-Leistungs-Verhältnis

128 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.7.1 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $3,999 MSRP

Mac Studio M1 Ultra 128GBApple-Upgrade

128 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.6.7 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $3,999 MSRP

AMD Instinct MI300A 128GBGrößter Sprung

128 GB VRAM (+32)5300 GB/s (+4481)

Makes the model fit on the accelerator instead of staying completely out of reach.56.5 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $12,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run GPT-OSS 120B?

Yes, Mac Studio M3 Ultra 96GB can run GPT-OSS 120B at NVFP4 quantization (Very compromised (needs ~10.1 GB host RAM)). The recommended Q4_K_M requires 87.5 GB which exceeds available memory, but at NVFP4 it needs only 81.7 GB. Expected decode speed: 7.5 tok/s.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 87.5 GB at Q4_K_M quantization. On Mac Studio M3 Ultra 96GB, it fits at NVFP4 using 81.7 GB.

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

The recommended quantization is Q4_K_M, but on Mac Studio M3 Ultra 96GB the best fitting quantization is NVFP4, which uses 81.7 GB.

What speed will GPT-OSS 120B run at on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, GPT-OSS 120B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25688ms using NVFP4 quantization.

Can Mac Studio M3 Ultra 96GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on Mac Studio M3 Ultra 96GB receives a F grade with 6.0 tok/s and 4K context.

What context window can GPT-OSS 120B use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, GPT-OSS 120B can safely use up to 4K tokens of context at NVFP4 quantization. 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 Mac Studio M3 Ultra 96GB?

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 Studio M3 Ultra 96GB as fast as VRAM for GPT-OSS 120B?

Not always. Mac Studio M3 Ultra 96GB 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 Studio M3 Ultra 96GB See all hardware for GPT-OSS 120B

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