Llama 4 Scout 17B 16E needs ~72.2 GB but RTX 5080 16GB only has 16.0 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
56.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.6 tok/s
TTFT
54090 ms
Safe context
4K
Memory
72.2 GB / 16.0 GB
Offload
80%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 72.2 GB, but this setup only exposes 16.0 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 | 3.6 tok/s | 29504 ms | 4K |
| Coding | F | Too heavy | 3.6 tok/s | 54090 ms | 4K |
| Agentic Coding | F | Too heavy | 3.6 tok/s | 78676 ms | 4K |
| Reasoning | F | Too heavy | 3.6 tok/s | 63924 ms | 4K |
| RAG | F | Too heavy | 3.6 tok/s | 98345 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on RTX 5080 16GB (16.0 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 |
Opciones de mejora
RTX PRO 6000 Blackwell Workstation Edition 96GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$9,999 MSRP
NVIDIA DGX Spark 128GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
No, Llama 4 Scout 17B 16E requires more memory than RTX 5080 16GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 72.2 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 5080 16GB, Llama 4 Scout 17B 16E achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 54090ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on RTX 5080 16GB receives a F grade with 3.6 tok/s and 4K context.
On RTX 5080 16GB, 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-5080-16gb" 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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