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
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VOOZH | about |
Llama 4 Maverick 17B 128E needs ~177.8 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q2_K quantization, expect ~118 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
85.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
39.9 tok/s
TTFT
4850 ms
Safe context
4K
Memory
265.8 GB / 180.0 GB
Offload
30%
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 40.3 tok/s | 2621 ms | 4K |
| Coding | F | Too heavy | 37.0 tok/s | 5238 ms | 4K |
| Agentic Coding | F | Too heavy | 39.2 tok/s | 7183 ms | 4K |
| Reasoning | F | Too heavy | 39.9 tok/s | 5732 ms | 4K |
| RAG | F | Too heavy | 39.2 tok/s | 8979 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on NVIDIA B200 180GB (180.0 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 |
Copy-paste commands to run Llama 4 Maverick 17B 128E on your machine.
Run
lms load Llama-4-Maverick-17B-128E-Instruct && lms server startUpgrade 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.
Adds memory headroom for longer context windows and future model growth.
~$20,000 MSRP
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 |