Llama 4 Scout 17B 16E needs ~84.1 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~9 tok/s.
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
Fit status
Tight fit
Decode
9.2 tok/s
TTFT
21095 ms
Safe context
60K
Memory
84.1 GB / 92.2 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 9.2 tok/s | 11506 ms | 60K |
| Coding | A | Tight fit | 9.2 tok/s | 21095 ms | 60K |
| Agentic Coding | A | Tight fit | 9.2 tok/s | 30684 ms | 60K |
| Reasoning | A | Tight fit | 9.2 tok/s | 24931 ms | 60K |
| RAG | A | Tight fit | 9.2 tok/s | 38355 ms | 60K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | A74 |
Q3_K_S | 3 | 53.4 GB | Low | A76 |
NVFP4 | 4 |
Copy-paste commands to run Llama 4 Scout 17B 16E on your machine.
Run
lms load Llama-4-Scout-17B-16E-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 3.3 tok/s | |
 Qwen 3.5 122B A10B | 122B | S |
Yes, MacBook Pro M3 Max 128GB can run Llama 4 Scout 17B 16E with a A grade (Tight fit). Expected decode speed: 9.2 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 84.1 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 M3 Max 128GB, Llama 4 Scout 17B 16E achieves approximately 9.2 tokens per second decode speed with a time-to-first-token of 21095ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on MacBook Pro M3 Max 128GB receives a A grade with 9.2 tok/s and 60K context.
On MacBook Pro M3 Max 128GB, Llama 4 Scout 17B 16E can safely use up to 60K tokens of context. The model's official context limit is 10.5M, 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/llama-4-scout-17b-16e-on-m3-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
61.0 GB |
| Medium |
| A76 |
Q4_K_MBest for your GPU | 4 | 66.5 GB | Medium | A76 |
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 |
| 15 tok/s |
| Mistral Small 4 119B | 119B | S | 16 tok/s |
 GPT-OSS 120B | 117B | A | 3.7 tok/s |
 Command A 111B | 111B | A | 3.9 tok/s |
Not always. MacBook Pro M3 Max 128GB 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.