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 | 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 |
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-Optionen
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.
ca. $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.
ca. $20,000 MSRP
Yes, NVIDIA B200 180GB can run Llama 4 Maverick 17B 128E at Q2_K quantization (Runs with offload). The recommended Q4_K_M requires 265.8 GB which exceeds available memory, but at Q2_K it needs only 177.8 GB. Expected decode speed: 118.4 tok/s.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 265.8 GB at Q4_K_M quantization. On NVIDIA B200 180GB, it fits at Q2_K using 177.8 GB.
The recommended quantization is Q4_K_M, but on NVIDIA B200 180GB the best fitting quantization is Q2_K, which uses 177.8 GB.
On NVIDIA B200 180GB, Llama 4 Maverick 17B 128E achieves approximately 118.4 tokens per second decode speed with a time-to-first-token of 1635ms using Q2_K quantization.
For coding workloads, Llama 4 Maverick 17B 128E on NVIDIA B200 180GB receives a F grade with 37.0 tok/s and 4K context.
On NVIDIA B200 180GB, Llama 4 Maverick 17B 128E can safely use up to 28K tokens of context at Q2_K quantization. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-maverick-17b-128e-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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