GPT-OSS 20B needs ~19.6 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 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
Tight fit
Decode
16.6 tok/s
TTFT
11672 ms
Safe context
38K
Memory
19.6 GB / 23.0 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 | 16.6 tok/s | 6367 ms | 38K |
| Coding | S | Tight fit | 15.4 tok/s | 12548 ms | 38K |
| Agentic Coding | S | Runs with offload | 16.6 tok/s | 16978 ms | 38K |
| Reasoning | S | Tight fit | 16.6 tok/s | 13795 ms | 38K |
| RAG | S | Runs with offload | 16.6 tok/s | 21222 ms | 38K |
How GPT-OSS 20B (21B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.2 GB | Low | S87 |
Q3_K_S | 3 | 10.3 GB | Low | S88 |
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 | A | 11.7 tok/s | ||
| 27B | S | 8.6 tok/s |
Yes, Mac mini M4 32GB can run GPT-OSS 20B with a S grade (Tight fit). Expected decode speed: 15.4 tok/s.
GPT-OSS 20B (21B parameters) requires approximately 19.6 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 Mac mini M4 32GB, GPT-OSS 20B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12548ms using Q4_K_M quantization.
For coding workloads, GPT-OSS 20B on Mac mini M4 32GB receives a S grade with 15.4 tok/s and 38K context.
On Mac mini M4 32GB, GPT-OSS 20B can safely use up to 38K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. Mac mini M4 32GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gpt-oss-20b-on-m4-mini-32gb" 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 |
| S89 |
Q4_K_M | 4 | 12.8 GB | Medium | S89 |
Q5_K_M | 5 | 15.1 GB | High | S88 |
Q6_KBest for your GPU | 6 | 17.2 GB | High | S88 |
Q8_0 | 8 | 22.5 GB | Very High | F0 |
F16 | 16 | 43.1 GB | Maximum | F0 |
| 27B | S | 7.1 tok/s |
| 30B | S | 12.4 tok/s |
| 35B | A | 10.2 tok/s |