GPT-OSS 20B needs ~26.5 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~45 tok/s.
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
Fit status
Runs well
Decode
44.5 tok/s
TTFT
4347 ms
Safe context
128K
Memory
26.5 GB / 69.1 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 44.5 tok/s | 2371 ms | 128K |
| Coding | S | Runs well | 44.5 tok/s | 4347 ms | 128K |
| Agentic Coding | S | Runs well | 44.5 tok/s | 6323 ms | 128K |
| Reasoning | S | Runs well | 44.5 tok/s | 5137 ms | 128K |
| RAG | S | Runs well | 44.5 tok/s | 7903 ms | 128K |
How GPT-OSS 20B (21B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.2 GB | Low | A79 |
Q3_K_S | 3 | 10.3 GB | Low | A79 |
NVFP4 | 4 |
Copy-paste commands to run GPT-OSS 20B on your machine.
Run
ollama run gpt-ossYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 35.1 tok/s | ||
| 27B | S | 15.2 tok/s |
Yes, MacBook Pro M2 Max 96GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 44.5 tok/s.
GPT-OSS 20B (21B parameters) requires approximately 26.5 GB of memory with Q4_K_M quantization.
The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, GPT-OSS 20B achieves approximately 44.5 tokens per second decode speed with a time-to-first-token of 4347ms using Q4_K_M quantization.
For coding workloads, GPT-OSS 20B on MacBook Pro M2 Max 96GB receives a S grade with 44.5 tok/s and 128K context.
On MacBook Pro M2 Max 96GB, GPT-OSS 20B can safely use up to 128K tokens of context. The model's official context limit is 128K, 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-20b-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
11.8 GB |
| Medium |
| A80 |
Q4_K_M | 4 | 12.8 GB | Medium | A80 |
Q5_K_M | 5 | 15.1 GB | High | A80 |
Q6_K | 6 | 17.2 GB | High | A81 |
Q8_0 | 8 | 22.5 GB | Very High | A82 |
F16Best for your GPU | 16 | 43.1 GB | Maximum | S86 |
| 27B | S | 11.6 tok/s |
| 35B | S | 32.4 tok/s |
| 30B | S | 36.3 tok/s |
Not always. MacBook Pro M2 Max 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.