Can the RTX 5080 run Ornith-1.5-9B?

Yes. Ornith-1.5-9B fits the RTX 5080's 16 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 92 tok/s. There's room for up to Q8_0 if you want higher quality.

VRAM
16 GB
Needs at Q4
7.8 GB
Headroom
8.2 GB
Speed (Q4_K_M)
~92 tok/s

Every quantization

Ornith-1.5-9B (9.7B) on 16 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M6 GB7.8 GBFits easily~92 tok/s
Q5_K_M7 GB8.8 GBFits easily~79 tok/s
Q8_010 GB11.8 GBFits~55 tok/s
FP1619 GB20.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5080's 960 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Ornith-1.5-9B details →

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Same model, other hardware - and other models on this card

Ornith-1.5-9B on other hardware

Also runs on the RTX 5080

Frequently asked

Quick answers

Can the NVIDIA RTX 5080 run Ornith-1.5-9B?

Yes. Ornith-1.5-9B fits the RTX 5080's 16 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 92 tok/s. There's room for up to Q8_0 if you want higher quality.

How much VRAM does Ornith-1.5-9B need?

About 6 GB of weights at Q4_K_M, 10 GB at Q8_0 and 19 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Ornith-1.5-9B on the RTX 5080?

Roughly 92 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5080's 960 GB/s memory bandwidth.