MacBook Pro M3 Max 128GBBudget pick
C5.6 tok/s decode
~$2,499 MSRP
Can it run?
Too heavy
Using Q4_K_M in Ollama
Fit status
Too heavy
Decode
11.9 tok/s
TTFT
16217 ms
Safe context
13K
Memory
59.6 GB / 48.0 GB
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Agentic Coding | F | Too heavy | 11.9 tok/s | 23588 ms | 22K |
| Chat | D | Very compromised (needs ~4.9 GB host RAM) | 10.8 tok/s | 9794 ms | 7K |
| Coding | F | Too heavy | 11.9 tok/s | 16217 ms | 13K |
| RAG | F | Too heavy | 11.9 tok/s | 29485 ms | 22K |
| Reasoning | F | Too heavy | 11.9 tok/s | 19165 ms | 13K |
How Llama 3.3 70B (70B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C42 |
Q3_K_SBest for your GPU | 3 | 34.3 GB | Low | C44 |
NVFP4 | 4 | 39.2 GB | Medium | C44 |
Q4_K_M | 4 | 42.7 GB | Medium | C44 |
Q5_K_M | 5 | 50.4 GB | High | F0 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Upgrade options
~$2,499 MSRP
~$2,499 MSRP
NVIDIA H100 80GBBiggest leap~$40,000 MSRP