Llama 4 Maverick 17B 128E needs ~252.6 GB but RTX PRO 5000 Blackwell 48GB only has 48.0 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
204.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.2 tok/s
TTFT
86268 ms
Safe context
4K
Memory
252.6 GB / 48.0 GB
Offload
80%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 252.6 GB, but this setup only exposes 48.0 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 | 2.2 tok/s | 47055 ms | 4K |
| Coding | F | Too heavy | 2.1 tok/s | 93169 ms | 4K |
| Agentic Coding | F | Too heavy | 2.2 tok/s | 125480 ms | 4K |
| Reasoning | F | Too heavy | 2.2 tok/s | 101953 ms | 4K |
| RAG | F | Too heavy | 2.2 tok/s | 156850 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 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 1623%.
~$20,000 MSRP
No, Llama 4 Maverick 17B 128E requires more memory than RTX PRO 5000 Blackwell 48GB provides.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 252.6 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 PRO 5000 Blackwell 48GB, Llama 4 Maverick 17B 128E achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 93169ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on RTX PRO 5000 Blackwell 48GB receives a F grade with 2.1 tok/s and 4K context.
On RTX PRO 5000 Blackwell 48GB, 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-pro-5000-blackwell-48gb" 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.
