Llama 4 Scout 17B 16E needs ~73.8 GB but MacBook Pro M2 Pro 32GB only has 23.0 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
50.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.4 tok/s
TTFT
80362 ms
Safe context
4K
Memory
73.8 GB / 23.0 GB
Offload
70%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 73.8 GB, but this setup only exposes 23.0 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 | 43834 ms | 4K |
| Coding | F | Too heavy | 2.4 tok/s | 80362 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116890 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 94974 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 146113 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | F0 |
Q3_K_S | 3 | 53.4 GB | Low | F0 |
NVFP4 | 4 | 61.0 GB | Medium | F0 |
Q4_K_M | 4 | 66.5 GB | Medium | F0 |
Q5_K_M | 5 | 78.5 GB | High | F0 |
Q6_K | 6 | 89.4 GB | High | F0 |
Q8_0 | 8 | 116.6 GB | Very High | F0 |
F16 | 16 | 223.5 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.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 333%.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 192%.
~$3,199 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.
~$15,000 MSRP
No, Llama 4 Scout 17B 16E requires more memory than MacBook Pro M2 Pro 32GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 73.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 4 Scout 17B 16E is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, Llama 4 Scout 17B 16E achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 80362ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on MacBook Pro M2 Pro 32GB receives a F grade with 2.4 tok/s and 4K context.
On MacBook Pro M2 Pro 32GB, Llama 4 Scout 17B 16E can safely use up to 4K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.
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 32GB 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.
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