MacBook Pro M4 32GBBudget pick
C90.7 tok/s decode
~$799 MSRP
Can it run?
Runs well
Using Q6_K in Ollama
Fit status
Runs well
Decode
151.2 tok/s
TTFT
1280 ms
Safe context
67K
Memory
3.8 GB / 16.0 GB
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Agentic Coding | C | Runs well | 163.8 tok/s | 1719 ms | 133K |
| Chat | C | Runs well | 163.8 tok/s | 645 ms | 33K |
| Coding | C | Runs well | 151.2 tok/s | 1280 ms | 67K |
| RAG | C | Runs well | 163.8 tok/s | 2149 ms | 133K |
| Reasoning | C | Runs well | 163.8 tok/s | 1397 ms | 67K |
How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | D30 |
Q3_K_S | 3 | 0.1 GB | Low | D30 |
NVFP4 | 4 |
huggingface-cli download hf-ggml-org--embeddinggemma-300m-ggufUpgrade options
~$799 MSRP
~$1,099 MSRP
| Medium |
| D30 |
Q4_K_M | 4 | 0.2 GB | Medium | D30 |
Q5_K_M | 5 | 0.2 GB | High | D30 |
Q6_K | 6 | 0.2 GB | High | D30 |
Q8_0 | 8 | 0.3 GB | Very High | D30 |
F16Best for your GPU | 16 | 0.6 GB | Maximum | D30 |
~$1,999 MSRP