GPT-OSS 20B needs ~18.7 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~33 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
1.4 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
35.1 tok/s
TTFT
5515 ms
Safe context
6K
Memory
18.7 GB / 17.3 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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.
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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 36.3 tok/s | 2908 ms | 6K |
| Coding | A | Very compromised | 32.7 tok/s | 5928 ms | 6K |
| Agentic Coding | F | Too heavy | 27.8 tok/s | 10117 ms | 6K |
| Reasoning | A | Very compromised | 32.7 tok/s | 7006 ms | 6K |
| RAG | F | Too heavy | 27.8 tok/s | 12646 ms | 6K |
How GPT-OSS 20B (21B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.2 GB | Low | S90 |
Q3_K_S | 3 | 10.3 GB | Low | S89 |
NVFP4 | 4 |
Copy-paste commands to run GPT-OSS 20B on your machine.
Run
ollama run gpt-ossYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Magistral Small 2507 | 24B | A | 17.8 tok/s | |
| Devstral Small 2 24B Instruct | 24B | A | 17.8 tok/s |
Yes, MacBook Pro M4 Pro 24GB can run GPT-OSS 20B with a A grade (Very compromised). Expected decode speed: 32.7 tok/s.
GPT-OSS 20B (21B parameters) requires approximately 18.7 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 M4 Pro 24GB, GPT-OSS 20B achieves approximately 32.7 tokens per second decode speed with a time-to-first-token of 5928ms using Q4_K_M quantization.
For coding workloads, GPT-OSS 20B on MacBook Pro M4 Pro 24GB receives a A grade with 32.7 tok/s and 6K context.
On MacBook Pro M4 Pro 24GB, GPT-OSS 20B can safely use up to 6K 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-m4-pro-24gb" 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_MBest for your GPU | 4 | 12.8 GB | Medium | S89 |
Q5_K_M | 5 | 15.1 GB | High | F0 |
Q6_K | 6 | 17.2 GB | High | F0 |
Q8_0 | 8 | 22.5 GB | Very High | F0 |
F16 | 16 | 43.1 GB | Maximum | F0 |
| Devstral Small 1.1 | 24B | A | 17.8 tok/s |
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.
Not always. MacBook Pro M4 Pro 24GB 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.