Can the NVIDIA RTX 5080 run Llama 4 Scout 17B-16E?
- FP16
- Q8_0
- Q6_K
- Q5_K_M
- Q4_K_M
- Q3_K_M
- Q2_K
No. Even at Q4_K_M, Llama 4 Scout 17B-16E needs about 68.3 GB, but the NVIDIA RTX 5080 has 16 GB usable. You could offload some layers to system RAM, but it will be very slow.
Run it on a rented GPU
On a cloud GPU with enough memory, the same command you'd use locally works:
ollama run llama4:scoutAt Q4_K_M it needs about 68.3 GB, so pick a GPU with at least 72 GB.
Run it in the cloud
Too big for the NVIDIA RTX 5080? Run Llama 4 Scout 17B-16E on a rented GPU instead.
Rent a cloud GPU by the hour. No upfront cost, stop any time.
Memory needed by quantization
At 8,192 tokens of context. The black line marks the 16 GB the NVIDIA RTX 5080 can use.
| Quant | Quality | Memory | Fits? | Speed | |
|---|---|---|---|---|---|
| FP16 | Full quality | 220.2 GB | Too big | — | |
| Q8_0 | Near-lossless | 118 GB | Too big | — | |
| Q6_K | Excellent | 91.6 GB | Too big | — | |
| Q5_K_M | Very good | 79.7 GB | Too big | — | |
| Q4_K_M | Good, most popular | 68.3 GB | Too big | — | |
| Q3_K_M | Noticeably degraded | 55.5 GB | Too big | — | |
| Q2_K | Heavily degraded | 43.1 GB | Too big | — |
GPUs that run Llama 4 Scout 17B-16E comfortably
Frequently asked questions
Can NVIDIA RTX 5080 run Llama 4 Scout 17B-16E?
No. Even at Q4_K_M, Llama 4 Scout 17B-16E needs about 68.3 GB, but the NVIDIA RTX 5080 has 16 GB usable. You could offload some layers to system RAM, but it will be very slow.
How much VRAM does Llama 4 Scout 17B-16E need?
About 68.3 GB at Q4_K_M with 8,192 tokens of context, and about 220.2 GB at full FP16 precision.
How fast is Llama 4 Scout 17B-16E on the NVIDIA RTX 5080?
It does not fit in the NVIDIA RTX 5080's memory, so most layers would run from system RAM and generation would be very slow.