Llama 4 Maverick 17B 128E needs ~249.4 GB but RTX 6000 Ada Laptop 16GB only has 16.0 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
233.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
249.4 GB / 16.0 GB
Offload
90%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 249.4 GB, but this setup only exposes 16.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 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 1795%.
~$20,000 MSRP
No, Llama 4 Maverick 17B 128E requires more memory than RTX 6000 Ada Laptop 16GB provides.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 249.4 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 RTX 6000 Ada Laptop 16GB, Llama 4 Maverick 17B 128E achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on RTX 6000 Ada Laptop 16GB receives a F grade with 2.0 tok/s and 4K context.
On RTX 6000 Ada Laptop 16GB, 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-rtx-6000-ada-laptop-16gb" 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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
