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.
~$1,099 MSRP
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VOOZH | about |
Falcon 40B Instruct needs ~33.6 GB but MacBook Pro M4 16GB only has 11.5 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
22.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.7 tok/s
TTFT
71824 ms
Safe context
4K
Memory
33.6 GB / 11.5 GB
Offload
70%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 33.6 GB, but this setup only exposes 11.5 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 | 2.7 tok/s | 39177 ms | 4K |
| Coding | F | Too heavy | 2.7 tok/s | 71824 ms | 4K |
| Agentic Coding | F | Too heavy | 2.7 tok/s | 104471 ms | 4K |
| Reasoning | F | Too heavy | 2.7 tok/s | 84883 ms | 4K |
| RAG | F | Too heavy | 2.7 tok/s | 130589 ms | 4K |
How Falcon 40B Instruct (40B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | F0 |
Q3_K_S | 3 | 19.6 GB | Low | F0 |
NVFP4 | 4 |
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.
~$1,099 MSRP
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.
~$1,599 MSRP
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.
~$2,499 MSRP
| Medium |
| F0 |
Q4_K_M | 4 | 24.4 GB | Medium | F0 |
Q5_K_M | 5 | 28.8 GB | High | F0 |
Q6_K | 6 | 32.8 GB | High | F0 |
Q8_0 | 8 | 42.8 GB | Very High | F0 |
F16 | 16 | 82.0 GB | Maximum | F0 |
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.