Llama 4 Scout 17B 16E needs ~74.6 GB but NVIDIA A100 40GB only has 40.0 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
34.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.1 tok/s
TTFT
19202 ms
Safe context
4K
Memory
74.6 GB / 40.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 74.6 GB, but this setup only exposes 40.0 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 | 10.5 tok/s | 10046 ms | 4K |
| Coding | F | Too heavy | 10.1 tok/s | 19202 ms | 4K |
| Agentic Coding | F | Too heavy | 9.3 tok/s | 30288 ms | 4K |
| Reasoning | F | Too heavy | 10.1 tok/s | 22693 ms | 4K |
| RAG | F | Too heavy | 9.3 tok/s | 37860 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | F0 |
Q3_K_S | 3 | 53.4 GB | Low | F0 |
NVFP4 | 4 | 61.0 GB | Medium | F0 |
Q4_K_M | 4 | 66.5 GB | Medium | F0 |
Q5_K_M | 5 | 78.5 GB | High | F0 |
Q6_K | 6 | 89.4 GB | High | F0 |
Q8_0 | 8 | 116.6 GB | Very High | F0 |
F16 | 16 | 223.5 GB | Maximum | F0 |
升级选项
RTX PRO 6000 Blackwell Workstation Edition 96GB经济之选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.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GB最佳性价比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.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA 升级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.
~$12,000 MSRP
No, Llama 4 Scout 17B 16E requires more memory than NVIDIA A100 40GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 74.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 4 Scout 17B 16E is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A100 40GB, Llama 4 Scout 17B 16E achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19202ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on NVIDIA A100 40GB receives a F grade with 10.1 tok/s and 4K context.
On NVIDIA A100 40GB, Llama 4 Scout 17B 16E can safely use up to 4K tokens of context. The model's official context limit is 10.5M, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-on-a100-40gb" 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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