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
16.8 tok/s
TTFT
11551 ms
Safe context
7K
Memory
57.2 GB / 24.0 GB
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Agentic Coding | F | Too heavy | 16.8 tok/s | 16801 ms | 11K |
| Chat | F | Too heavy | 16.8 tok/s | 6300 ms | 4K |
| Coding | F | Too heavy | 16.8 tok/s | 11551 ms | 7K |
| RAG | F | Too heavy | 16.8 tok/s | 21002 ms | 11K |
| Reasoning | F | Too heavy | 16.8 tok/s | 13651 ms | 7K |
How Llama 3.3 70B (70B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | F0 |
Q3_K_S | 3 | 34.3 GB | Low | F0 |
NVFP4 | 4 | 39.2 GB | Medium | F0 |
Q4_K_M | 4 | 42.7 GB | Medium | F0 |
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
NVIDIA GH200 96GBNVIDIA upgrade