Mac mini M4 64GBBudget pick
C4.4 tok/s decode
~$1,099 MSRP
Can it run?
Very compromised (needs ~2.9 GB host RAM)
Using Q4_K_M in Ollama
Fit status
Very compromised (needs ~2.9 GB host RAM)
Decode
25.5 tok/s
TTFT
7584 ms
Safe context
14K
Memory
28.1 GB / 24.0 GB
Offload
10%
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Agentic Coding | F | Too heavy | 28.9 tok/s | 9738 ms | 23K |
| Chat | C | Runs with offload (needs ~1.2 GB host RAM) | 27.5 tok/s | 3846 ms | 7K |
| Coding | D | Very compromised (needs ~2.9 GB host RAM) | 25.5 tok/s | 7584 ms | 14K |
| RAG | F | Too heavy | 28.9 tok/s | 12172 ms | 23K |
| Reasoning | D | Very compromised (needs ~2.9 GB host RAM) | 25.5 tok/s | 8963 ms |
How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C41 |
Q3_K_S | 3 | 15.7 GB | Low | C43 |
NVFP4Best for your GPU |
ollama run qwen-2.5-coder-32bhuggingface-cli download qwen-2.5-coder-32bUpgrade options
~$1,099 MSRP
~$1,599 MSRP
NVIDIA A100 40GBBiggest leap| 14K |
| 4 |
17.9 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 19.5 GB | Medium | C44 |
Q5_K_M | 5 | 23.0 GB | High | C44 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
~$10,000 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA upgrade