GPT-OSS 120B needs ~82.3 GB but MacBook Pro M4 Pro 48GB only has 34.6 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
47.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.6 tok/s
TTFT
74620 ms
Safe context
4K
Memory
82.3 GB / 34.6 GB
Offload
60%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 82.3 GB, but this setup only exposes 34.6 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 | 2.6 tok/s | 40702 ms | 4K |
| Coding | F | Too heavy | 2.6 tok/s | 74620 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 108538 ms | 4K |
| Reasoning | F | Too heavy | 2.6 tok/s | 88187 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 135672 ms | 4K |
How GPT-OSS 120B (117B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 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 MacBook Pro M4 Pro 48GB provides.
GPT-OSS 120B (117B parameters) requires approximately 82.3 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 MacBook Pro M4 Pro 48GB, GPT-OSS 120B achieves approximately 2.6 tokens per second decode speed with a time-to-first-token of 74620ms using Q4_K_M quantization.
For coding workloads, GPT-OSS 120B on MacBook Pro M4 Pro 48GB receives a F grade with 2.6 tok/s and 4K context.
On MacBook Pro M4 Pro 48GB, 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-m4-pro-48gb" 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. MacBook Pro M4 Pro 48GB 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.
