Llama 4 Maverick 17B 128E needs ~261.9 GB but NVIDIA H200 PCIe 141GB only has 141.0 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
120.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
11.6 tok/s
TTFT
16761 ms
Safe context
4K
Memory
261.9 GB / 141.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 261.9 GB, but this setup only exposes 141.0 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 | 11.7 tok/s | 9038 ms | 4K |
| Coding | F | Too heavy | 11.6 tok/s | 16761 ms | 4K |
| Agentic Coding | F | Too heavy | 11.3 tok/s | 24939 ms | 4K |
| Reasoning | F | Too heavy | 11.6 tok/s | 19808 ms | 4K |
| RAG | F | Too heavy | 11.3 tok/s | 31173 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 4 Maverick 17B 128E at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~7 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 6.7 | Too big |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 4.3 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 4.1 | Too big |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 3.2 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 3.2 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 2.4 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 2.3 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.2 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 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 |
アップグレードオプション
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 227%.
〜$20,000 MSRP
No, Llama 4 Maverick 17B 128E requires more memory than NVIDIA H200 PCIe 141GB provides.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 261.9 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 NVIDIA H200 PCIe 141GB, Llama 4 Maverick 17B 128E achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16761ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on NVIDIA H200 PCIe 141GB receives a F grade with 11.6 tok/s and 4K context.
On NVIDIA H200 PCIe 141GB, 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.
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.
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