How fast will Ornith 1.5 9B feel on the H100 80GB (cloud)?

Ornith 1.5 9B on the H100 80GB (cloud)

Ornith 1.5 9B is a 9.65B-parameter model. At Q4_K_M it needs about 6.5 GB of memory. The H100 80GB (cloud) has 74 GB usable, so it fits entirely in memory. Expect about 180 tokens/sec (153–207), with the first token after 0.3 s for a short prompt. This estimate is projected from the hardware specs; no benchmarks exist yet.

Speed by quantization

Quantization	File size	Memory	Generation	First token	Source
Q4_K_M	5.8 GB	Fits	180 tok/s	0.3 s	Projected
Q8_0	9.8 GB	Fits	106 tok/s	0.3 s	Projected
FP16	19.3 GB	Fits	54 tok/s	0.3 s	Projected

Ornith 1.5 9B on other hardware

H200 141GB (cloud) · 258 tok/s at Q4_K_M
A100 80GB (cloud) · 110 tok/s at Q4_K_M
Mac Studio M5 Ultra 512GB · 98 tok/s at Q4_K_M
NVIDIA RTX 5090 32GB · 96 tok/s at Q4_K_M
NVIDIA RTX PRO 6000 96GB · 96 tok/s at Q4_K_M
AMD RX 7900 XTX 24GB · 95 tok/s at Q4_K_M
AMD RX 7900 XT 20GB · 79 tok/s at Q4_K_M
Mac Studio M3 Ultra 512GB · 67 tok/s at Q4_K_M
AMD RX 9070 XT 16GB · 64 tok/s at Q4_K_M
AMD Radeon AI PRO R9700 32GB · 64 tok/s at Q4_K_M
NVIDIA RTX 4090 24GB · 54 tok/s at Q4_K_M
NVIDIA RTX 5080 16GB · 52 tok/s at Q4_K_M
NVIDIA RTX 3090 24GB · 50 tok/s at Q4_K_M
MacBook Pro M5 Max 128GB · 50 tok/s at Q4_K_M
NVIDIA RTX 5070 Ti 16GB · 48 tok/s at Q4_K_M
Mac Studio M4 Max 128GB · 45 tok/s at Q4_K_M
NVIDIA RTX 4070 Ti Super 16GB · 36 tok/s at Q4_K_M
MacBook M3 Max 48GB · 33 tok/s at Q4_K_M
NVIDIA RTX 4070 12GB · 27 tok/s at Q4_K_M
AMD Ryzen AI Max+ 395 128GB (Strix Halo) · 25 tok/s at Q4_K_M
MacBook Pro M5 Pro 48GB · 25 tok/s at Q4_K_M
NVIDIA RTX 5060 Ti 16GB · 24 tok/s at Q4_K_M
Mac Mini M4 Pro 64GB · 22 tok/s at Q4_K_M
MacBook M4 Pro 24GB · 22 tok/s at Q4_K_M
Intel Arc Pro B70 32GB · 20 tok/s at Q4_K_M
NVIDIA RTX 3060 12GB · 19 tok/s at Q4_K_M
MacBook M2 Pro 16GB · 16 tok/s at Q4_K_M
NVIDIA DGX Spark 128GB · 15 tok/s at Q4_K_M
CPU-only (Ryzen 9 9950X, 96GB DDR5) · 10 tok/s at Q4_K_M

Other models on the H100 80GB (cloud)

Qwen 3.6/3.8 27B · 80 tok/s at Q4_K_M
GLM-5.3 Flash · won't run
GPT-OSS 20B · 648 tok/s at MXFP4
Gemma 4 26B A4B · 541 tok/s at Q4_K_M
Qwen 3.5/3.6 35B A3B · 731 tok/s at Q4_K_M
Ornith 1.5 35B A3B · 575 tok/s at Q4_K_M
DeepSeek V4 Flash · won't run
GPT-OSS 120B · 488 tok/s at MXFP4
Gemma 4 31B · 74 tok/s at Q4_K_M
Llama 3.1 8B · 124 tok/s at Q4_K_M
Qwen 3.5 9B · 183 tok/s at Q4_K_M
DeepSeek R1 Distill 14B · 75 tok/s at Q4_K_M
Muse Glimmer 30B · 53 tok/s at Q4_K_M
Qwen3 30B A3B · 519 tok/s at Q4_K_M

All models on the H100 80GB (cloud) · Ornith 1.5 9B on all hardware

Estimates for a short prompt on a single device. Measured numbers come from community benchmarks (r/LocalLLaMA, localscore.ai and more); others are derived from similar setups or projected from specs. Open source on GitHub.