Llama 4 Scout 17B 16E needs ~80.7 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~7 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
11.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~9.5 GB host RAM)
Decode
7.0 tok/s
TTFT
27673 ms
Safe context
4K
Memory
80.7 GB / 69.1 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 9.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~8.5 GB host RAM) | 7.2 tok/s | 14741 ms | 4K |
| Coding | B | Very compromised (needs ~9.5 GB host RAM) | 7.0 tok/s | 27673 ms | 4K |
| Agentic Coding | F | Too heavy | 6.7 tok/s | 42106 ms | 4K |
| Reasoning | B | Very compromised (needs ~9.5 GB host RAM) | 7.0 tok/s | 32705 ms | 4K |
| RAG | F | Too heavy | 6.7 tok/s | 52633 ms |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | A76 |
Q3_K_SBest for your GPU | 3 | 53.4 GB | Low | A76 |
Copy-paste commands to run Llama 4 Scout 17B 16E on your machine.
Run
lms load Llama-4-Scout-17B-16E-Instruct && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 31%.
~$2,499 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 153%.
~$3,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 140%.
~$3,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1263%.
~$15,000 MSRP
Yes, MacBook Pro M2 Max 96GB can run Llama 4 Scout 17B 16E with a B grade (Very compromised (needs ~9.5 GB host RAM)). Expected decode speed: 7.0 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 80.7 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 Max 96GB, Llama 4 Scout 17B 16E achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27673ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on MacBook Pro M2 Max 96GB receives a B grade with 7.0 tok/s and 4K context.
On MacBook Pro M2 Max 96GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M2 Max 96GB 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.