Llama 4 Maverick 17B 128E needs ~258.2 GB but MacBook Pro M4 Max 96GB only has 69.1 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
189.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
60889 ms
Safe context
4K
Memory
258.2 GB / 69.1 GB
Offload
70%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 258.2 GB, but this setup only exposes 69.1 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 | 3.2 tok/s | 33212 ms | 4K |
| Coding | F | Too heavy | 2.9 tok/s | 65760 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 88566 ms | 4K |
| Reasoning | F | Too heavy | 3.2 tok/s | 71960 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 110708 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 156.0 GB | Low | F0 |
Q3_K_S | 3 | 196.0 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.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1084%.
~$20,000 MSRP
No, Llama 4 Maverick 17B 128E requires more memory than MacBook Pro M4 Max 96GB provides.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 258.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 4 Maverick 17B 128E is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 96GB, Llama 4 Maverick 17B 128E achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 65760ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on MacBook Pro M4 Max 96GB receives a F grade with 2.9 tok/s and 4K context.
On MacBook Pro M4 Max 96GB, Llama 4 Maverick 17B 128E can safely use up to 4K tokens of context. The model's official context limit is 1.0M, 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-maverick-17b-128e-on-m4-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:
224.0 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 244.0 GB | Medium | F0 |
Q5_K_M | 5 | 288.0 GB | High | F0 |
Q6_K | 6 | 328.0 GB | High | F0 |
Q8_0 | 8 | 428.0 GB | Very High | F0 |
F16 | 16 | 820.0 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 M4 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.
