Falcon 40B Instruct needs ~33.6 GB but MacBook Pro M2 Pro 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
2.4 tok/s
TTFT
79786 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 | 2.4 tok/s | 43520 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 86768 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116053 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 94293 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 145066 ms | 4K |
How Falcon 40B Instruct (40B params) fits at each quantization level on MacBook Pro M2 Pro 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
No, Falcon 40B Instruct requires more memory than MacBook Pro M2 Pro 16GB provides.
Falcon 40B Instruct (40B parameters) requires approximately 33.6 GB of memory with Q5_K_M quantization.
The recommended quantization for Falcon 40B Instruct is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Falcon 40B Instruct achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 86768ms using Q5_K_M quantization.
For coding workloads, Falcon 40B Instruct on MacBook Pro M2 Pro 16GB receives a F grade with 2.2 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, Falcon 40B Instruct can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/falcon-40b-instruct-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
22.4 GB |
| 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.
Not always. MacBook Pro M2 Pro 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
