Can the RTX 2000 Ada run Ornith-1.5-9B-OBLITERATED?

Yes. Ornith-1.5-9B-OBLITERATED fits the RTX 2000 Ada's 16 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 25 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)
~25 tok/s

Every quantization

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

QuantWeightsNeedsFitEst. speed
Q4_K_M6 GB7.8 GBFits easily~25 tok/s
Q5_K_M7 GB8.8 GBFits easily~21 tok/s
Q8_010 GB11.8 GBFits~15 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 2000 Ada's 256 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 mradermacher/Ornith-1.5-9B-OBLITERATED-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-OBLITERATED details →

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

Ornith-1.5-9B-OBLITERATED on other hardware

Also runs on the RTX 2000 Ada

Frequently asked

Quick answers

Can the RTX 2000 Ada Generation run Ornith-1.5-9B-OBLITERATED?

Yes. Ornith-1.5-9B-OBLITERATED fits the RTX 2000 Ada's 16 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 25 tok/s. There's room for up to Q8_0 if you want higher quality.

How much VRAM does Ornith-1.5-9B-OBLITERATED 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-OBLITERATED on the RTX 2000 Ada?

Roughly 25 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 2000 Ada's 256 GB/s memory bandwidth.