Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
![]() |
VOOZH | about |
Llama 4 Maverick 17B 128E needs ~254.7 GB but MacBook Pro M1 Max 64GB only has 46.1 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
208.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
95203 ms
Safe context
4K
Memory
254.7 GB / 46.1 GB
Offload
80%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 254.7 GB, but this setup only exposes 46.1 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 51929 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 95203 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 138477 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 112513 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 173097 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on MacBook Pro M1 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 156.0 GB | Low | F0 |
Q3_K_S | 3 | 196.0 GB | Low | F0 |
NVFP4 | 4 | 224.0 GB | Medium | F0 |
Q4_K_M | 4 | 244.0 GB | Medium | F0 |
Q5_K_M | 5 | 288.0 GB | High | F0 |
Q6_K | 6 | 328.0 GB | High | F0 |
Q8_0 | 8 | 428.0 GB | Very High | F0 |
F16 | 16 | 820.0 GB | Maximum | F0 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1795%.
~$20,000 MSRP