GPT-OSS 120B needs ~84.1 GB but Mac Studio M2 Ultra 64GB only has 46.1 GB. Try a smaller quantization or lighter model.
Operating mode
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.
Select quantization to explore
38.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.3 tok/s
TTFT
58219 ms
Safe context
4K
Memory
84.1 GB / 46.1 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 84.1 GB, but this setup only exposes 46.1 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.4 tok/s | 30747 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 63313 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 88509 ms | 4K |
| Reasoning | F | Too heavy | 3.3 tok/s | 68804 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 110637 ms | 4K |
How GPT-OSS 120B (117B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 45.6 GB | Low | F0 |
Q3_K_S | 3 | 57.3 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
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.
~$2,499 MSRP
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.
~$3,999 MSRP
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.
~$3,999 MSRP
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.
~$12,000 MSRP
No, GPT-OSS 120B requires more memory than Mac Studio M2 Ultra 64GB provides.
GPT-OSS 120B (117B parameters) requires approximately 84.1 GB of memory with Q4_K_M quantization.
The recommended quantization for GPT-OSS 120B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 64GB, GPT-OSS 120B achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 63313ms using Q4_K_M quantization.
For coding workloads, GPT-OSS 120B on Mac Studio M2 Ultra 64GB receives a F grade with 3.1 tok/s and 4K context.
On Mac Studio M2 Ultra 64GB, GPT-OSS 120B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gpt-oss-120b-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. Mac Studio M2 Ultra 64GB 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.
