Ornith 1.5 35B-A3B

Headlines · 2

1. 3d
   Ornith 1.5 tuned for long-context inference on one Tesla P40

   Developer sargetun123 reported running Ornith 1.5 35B-A3B on a single 24GB Tesla P40 using a custom llama.cpp fork, Q4_0 weights, q8_0 KV cache and MTP drafting. The reported setup supports a full 262k context and reached about 60.8 tokens/s decode without MTP at zero context and 29.1 tokens/s at 100k context, while a real server with MTP generated 35–39 tokens/s for a 101k-token prompt. The developer also reported that disabling ECC freed 1.5GB of VRAM and improved decoding by about 9%.

   r/LocalLLaMA · 🔥 0
2. Oct 23
   Local MoE models show large gaps in agentic coding efficiency

   A Reddit author benchmarked Flint 40B-A2.5B, Ornith 1.5 35B-A3B, and Nemotron 3.5 Lightning 30B-A3B as parallel coding workers under Opus 5.5 in Atomic Agent v0.6.10. Flint completed the Three.js task in 7:36 with six fixes and $0.41 in Opus usage, versus 9:04, 14 fixes, and $0.49 for Ornith, and 30:32, 16 fixes, and $0.95 for Nemotron. The results are preliminary because each model ran only once, output quality was not assessed in detail, and the author works on the agent runtime.

   r/LocalLLaMA · 🔥 0

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