Llama 4 Scout 17B 16E needs ~73.0 GB but RTX 4090 24GB only has 24.0 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
49.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.4 tok/s
TTFT
44058 ms
Safe context
4K
Memory
73.0 GB / 24.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 73.0 GB, but this setup only exposes 24.0 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 | 4.4 tok/s | 24032 ms | 4K |
| Coding | F | Too heavy | 4.1 tok/s | 47583 ms | 4K |
| Agentic Coding | F | Too heavy | 4.4 tok/s | 64085 ms | 4K |
| Reasoning | F | Too heavy | 4.4 tok/s | 52069 ms | 4K |
| RAG | F | Too heavy | 4.4 tok/s | 80106 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on RTX 4090 24GB (24.0 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 DGX Spark 128GBNVIDIA 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.
No, Llama 4 Scout 17B 16E requires more memory than RTX 4090 24GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 73.0 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 RTX 4090 24GB, Llama 4 Scout 17B 16E achieves approximately 4.1 tokens per second decode speed with a time-to-first-token of 47583ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on RTX 4090 24GB receives a F grade with 4.1 tok/s and 4K context.
On RTX 4090 24GB, 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-rtx-4090-24gb" 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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