Advisor / Models / Ornith 1.5 9B

Ornith 1.5 9B: VRAM requirements & speed

Ornith 1.5 9B (9.65B dense, mit). Native context 256k. Smallest GGUF: 5.91 GB.

5.91 GBsmallest quant file
28hardware pages that fit it fully
0run with offload
2measured data points

Memory needed by quantization and context

GGUF file size plus f16 KV cache (and fixed overhead of ~1 GB per GPU not shown). Source: bartowski/Ornith-1.5-9B-GGUF.

QuantFile+8k ctx+32k ctx+128k ctx+256k ctx
Q4_K_M5.91 GB6.2 GB7.0 GB10.3 GB14.6 GB
Q5_K_M6.85 GB7.2 GB8.0 GB11.2 GB15.5 GB
Q6_K7.7 GB8.0 GB8.8 GB12.0 GB16.3 GB
Q8_09.55 GB9.9 GB10.7 GB13.9 GB18.2 GB

KV cache size by context length

Computed from the model's attention layout, including a fixed 0.05 GB linear-attention state.

Contextf16q8_0q4_0
8k0.32 GB0.20 GB0.13 GB
32k1.13 GB0.62 GB0.35 GB
128k4.35 GB2.33 GB1.26 GB
256k8.64 GB4.62 GB2.47 GB

Which hardware runs Ornith 1.5 9B? · 16k context

HardwareQuantFitMemoryTok/sSpeed source
RTX PRO 6000 96GBQ8_0fits fully10.1 GB153scaled from measured
RTX 5090 32GBQ8_0fits fully10.1 GB145scaled from measured
RTX 4090 24GBQ8_0fits fully10.1 GB95scaled from measured
RTX 5080 16GBQ8_0fits fully10.1 GB92scaled from measured
RTX 5070 Ti 16GBQ8_0fits fully10.1 GB86scaled from measured
RTX 3090 24GBQ8_0fits fully10.1 GB85scaled from measured
Mac Studio M5 Ultra 256GBQ8_0fits fully10.1 GB78estimated
Mac Studio M5 Ultra 512GB (ships late Oct 2026)Q8_0fits fully10.1 GB78estimated
RTX 5070 12GBQ8_0fits fully10.1 GB67scaled from measured
2x RTX 3090 (48GB total)Q8_0fits fully10.1 GB67estimated
RTX 4070 12GBQ8_0fits fully10.1 GB51scaled from measured
RTX 5060 Ti 16GBQ8_0fits fully10.1 GB46scaled from measured
8GB VRAM class (RTX 4060 8GB, 32GB RAM)Q4_K_Mfits fully6.5 GB44scaled from measured
Mac Studio M5 Max 128GBQ8_0fits fully10.1 GB39scaled from measured
MacBook Pro 14/16 M5 Max 128GBQ8_0fits fully10.1 GB39scaled from measured
RTX 3060 12GB (32GB RAM)Q8_0fits fully10.1 GB36scaled from measured
Mac Studio (2025) M3 Ultra 96GBQ8_0fits fully10.1 GB34scaled from measured
16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)Q8_0fits fully10.1 GB30scaled from measured
MacBook Pro 14/16 (2024) M4 Max 128GBQ8_0fits fully10.1 GB27scaled from measured
AMD RX 7900 XTX 24GBQ8_0fits fully10.1 GB25scaled from measured
AMD Ryzen AI Max+ PRO 495 (Ryzen AI Halo) 192GBQ8_0fits fully10.1 GB23estimated
MacBook Pro 14/16 M5 Pro 48GBQ8_0fits fully10.1 GB22scaled from measured
NVIDIA DGX Spark 128GBQ8_0fits fully10.1 GB17scaled from measured
MacBook Air M5 32GBQ4_K_Mfits fully6.5 GB17scaled from measured
Mac mini (2024) M4 Pro 64GBQ8_0fits fully10.1 GB16scaled from measured
Mac mini M6 32GBQ6_Kfits fully8.3 GB16scaled from measured
AMD Ryzen AI Max+ 395 (Strix Halo) 128GBQ5_K_Mfits fully7.4 GB15scaled from measured
Mac mini / iMac M4 16GBQ4_K_Mfits fully6.5 GB13scaled from measured

Discrete GPUs are modelled with 32 GB of system RAM available for offload. Fits (at least with offload) on every listed configuration.

MLX conversions for Apple Silicon

No MLX conversion yet: we found no verified mlx-community 4-bit or 8-bit repo for Ornith 1.5 9B. Use the GGUF files with llama.cpp or ollama on a Mac. How we check.

Links

Model card · GGUF files · Open in the advisor · last verified 2026-10-20

FAQ

How much VRAM does Ornith 1.5 9B need?

The smallest quant (Q4_K_M) is 5.91 GB, so you need roughly 7 GB for an 8k context including KV cache and overhead. At 256k context the f16 KV cache alone is 8.6 GB.

Can I run Ornith 1.5 9B on a RTX PRO 6000 96GB?

Yes: on RTX PRO 6000 96GB it fits fully as Q8_0 at about 153 tok/s (scaled from measured). 28 of 28 hardware configurations fit it fully at 16k context.

What is the maximum context for Ornith 1.5 9B?

256k tokens natively. Memory, not the model, is usually the limit: see the KV cache table above.

All models: model index · by memory: 8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB. Numbers use the same fit math as the advisor; sources and methods.