Llama 4 Scout 17B 16E needs ~72.0 GB but MacBook Pro M1 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
60.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.2 tok/s
TTFT
86544 ms
Safe context
4K
Memory
72.0 GB / 11.5 GB
Offload
80%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 72.0 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 | 2.2 tok/s | 47206 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 86544 ms | 4K |
| Agentic Coding | F | Too heavy | 2.2 tok/s | 125882 ms | 4K |
| Reasoning | F | Too heavy | 2.2 tok/s | 102279 ms | 4K |
| RAG | F | Too heavy | 2.2 tok/s | 157353 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 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 |
Opciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$2,499 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 373%.
~$2,499 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 218%.
~$3,199 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$15,000 MSRP
No, Llama 4 Scout 17B 16E requires more memory than MacBook Pro M1 Pro 16GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 72.0 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 M1 Pro 16GB, Llama 4 Scout 17B 16E achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 86544ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on MacBook Pro M1 Pro 16GB receives a F grade with 2.2 tok/s and 4K context.
On MacBook Pro M1 Pro 16GB, 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 M1 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.
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