Mac mini M4 64GBBudget pick
C90.7 tok/s decode
~$1,099 MSRP
Can it run?
Runs well
Using Q6_K in Ollama
Fit status
Runs well
Decode
483.8 tok/s
TTFT
400 ms
Safe context
85K
Memory
6.0 GB / 32.0 GB
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Agentic Coding | C | Runs well | 483.8 tok/s | 582 ms | 170K |
| Chat | C | Runs well | 483.8 tok/s | 350 ms | 43K |
| Coding | C | Runs well | 483.8 tok/s | 400 ms | 85K |
| RAG | C | Runs well | 483.8 tok/s | 728 ms | 170K |
| Reasoning | C | Runs well | 483.8 tok/s | 473 ms | 85K |
How Llama 3.2 1B Instruct Q8 0 (1B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | D30 |
Q3_K_S | 3 | 0.5 GB | Low | D30 |
NVFP4 | 4 | 0.6 GB | Medium | D30 |
Q4_K_M | 4 | 0.6 GB | Medium | D30 |
Q5_K_M | 5 | 0.7 GB | High | D30 |
Q6_K | 6 | 0.8 GB | High | D30 |
Q8_0 | 8 | 1.1 GB | Very High | D30 |
F16Best for your GPU | 16 | 2.1 GB | Maximum | D31 |
huggingface-cli download hf-hugging-quants--llama-3-2-1b-instruct-q8-0-ggufUpgrade options
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