MacBook Pro M4 Pro 64GBBudget pick
C10.8 tok/s decode
~$1,599 MSRP
Can it run?
Tight fit
Using Q4_K_M in Ollama
Fit status
Tight fit
Decode
19.3 tok/s
TTFT
10008 ms
Safe context
18K
Memory
28.9 GB / 32.0 GB
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Agentic Coding | D | Runs with offload (needs ~1.1 GB host RAM) | 18.5 tok/s | 15248 ms | 30K |
| Chat | C | Tight fit | 19.3 tok/s | 5459 ms | 10K |
| Coding | C | Tight fit | 19.3 tok/s | 10008 ms | 18K |
| RAG | D | Runs with offload (needs ~1.1 GB host RAM) | 18.5 tok/s | 19060 ms | 30K |
| Reasoning | C | Tight fit | 19.3 tok/s | 11828 ms | 18K |
How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | D38 |
Q3_K_S | 3 | 15.7 GB | Low | D40 |
NVFP4 | 4 | 17.9 GB | Medium | C41 |
Q4_K_M | 4 | 19.5 GB | Medium | C42 |
Q5_K_MBest for your GPU | 5 | 23.0 GB | High | C44 |
Q6_K | 6 | 26.2 GB | High | C44 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
ollama run qwen-2.5-coder-32bhuggingface-cli download qwen-2.5-coder-32bUpgrade options
~$1,599 MSRP
~$2,499 MSRP
NVIDIA A100 40GBBiggest leap~$10,000 MSRP