Ornith 1.5 9B VRAM requirements

How much VRAM does Ornith 1.5 9B need?

Ornith 1.5 9B needs 6.76 GB at Q4_K_M, 11.3 GB at Q8_0 and 20.6 GB at FP16 with 8,192 tokens of context and one request. Each total below is the weights plus the FP16 KV cache and the runtime overhead (0.5 GB plus 10%), and each row opens the calculator with that setting. What the GGUF names mean.

Fine-tuning Ornith 1.5 9B? Ornith 1.5 9B VRAM for LoRA, QLoRA and full training.

Run Ornith 1.5 9B with llama.cpp

llama-server -hf bartowski/Ornith-1.5-9B-GGUF:Q4_K_M -c 262144 -ngl 99

Ornith-1.5-9B-Q4_K_M.gguf, 5.9 GB, from bartowski/Ornith-1.5-9B-GGUF (checked 2026-09-29). On a 24 GB RTX 3090 / 4090, Q4_K_M with -c 262144 takes 15.4 GB and leaves 8.64 GB.

Ornith 1.5 9B at 8K, 32K, 128K and 256K (full) tokens of context

8 of its 32 layers use full attention and 24 are linear-attention layers with no growing cache. Each extra token of context adds 32 KB of FP16 cache per request. The last column is the smallest setup here, one card or a group, that holds it at Q4_K_M. How the KV cache works.

How fast Ornith 1.5 9B writes

Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth. A dash means it does not fit on one card. Try other settings in the speed calculator.

Longest context on one GPU

How many tokens of context Ornith 1.5 9B fits on a single card with one request, an FP16 KV cache and 0.5 GB left free. "Full" means the model's whole context window fits.

Model details

Parameters

9.7B (9,653,104,368)

Layers

8 of its 32 layers use full attention and 24 are linear-attention layers with no growing cache

Attention cache

4 KV heads × 256

Context length

262,144 tokens

Published weights

18.0 GB (BF16)

On Hugging Face

ornith-ai/Ornith-1.5-9B

Ornith 1.5 9B next to similar-sized models

The three models here closest to it in parameter count, at Q4_K_M with 8,192 tokens of context.