LOADING DATASHEET
LOADING DATASHEET
by Ornith Ai
RANKED #49 OF 271 ASSISTANTS · OVERALL #80 OF 6,567 · VIBE SCORE 6.5 · 1,024 VOICES
Ornith 1.5 35B A3B is an open-weight mixture-of-experts model for agentic coding, tool use, image understanding, and long-context work. This variant disables thinking for faster direct responses.
31 mentions
12 mentions
26 weeks · 1,101 voices
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download Ornith 1.5 35B once and it's yours. No subscription, no rate limits, works offline.
36B parameters (MoE with ~3B active per token), a Q4 file is about 20 GB
NVIDIA GeForce RTX 4060 Ti 16GB
16 GB VRAM + 32 GB SYSTEM RAM
~$450IQ3_XS / Compact quant, ~14 tok/s, 8k context with partial CPU MoE offloading
NVIDIA GeForce RTX 3090 24GB
24 GB VRAM
~$700 USEDQ4_K_M quant, ~32 tok/s, 16k context entirely in VRAM
Apple Mac Studio M2 Ultra (128GB)
128 GB UNIFIED MEMORY
~$3,300 USEDBF16 / FP16 full precision, ~45 tok/s, 128k context
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long Ornith 1.5 35B takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce RTX 4060 Ti 16GB | SWEET SPOTNVIDIA GeForce RTX 3090 24GB | FULL POWERApple Mac Studio M2 Ultra (128GB) |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~36 S★★★★★ | ~16 S★★★★★ | ~11 S★★★★★ |
| Summarize a documenta long report boiled down to the points that matter | ~1.8 MIN★★★★★ | ~47 S★★★★★ | ~33 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~9.5 MIN★★★★★ | ~4.2 MIN★★★★★ | ~3 MIN★★★★★ |
| Build a backendan API with routes, storage and tests | ~30 MIN★★★★★ | ~13 MIN★★★★★ | ~9.3 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~18 MIN★★★★★ | ~7.8 MIN★★★★★ | ~5.6 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
Not enough discussion about Ornith 1.5 35B yet to call it. We found 1024 posts but people did not say much either way.
112 POSTS · 912 COMMENTS · REDDIT · X · GITHUB · LEMMY · HACKER NEWS
Checked picks first: tags say why you would switch, stars say how fully each one stands in for Ornith 1.5 35B.
We have Q3.8 35B at home: 3x new Ornith 1.5 released
Playing with Ornith 1.5 35B like crazy ~30M tokens just today with oMLX! This model is super fast locally on Apple Silic
DGX SPARK USERS, check this it could happen to you. Yesterday I finally come across the DGX Spark issue people have been
Local AI News You Missed - August 2026
Very quick comparison between Ornith 1.5 35B MoE and Qwen 3.8 27B Dense. 📣 Clearly it's not a fair one, but let's in an
This is for 3060 users! (or anyone with 8 - 24GB vram) I built this repo so fellow 3060 folks don't have to waste hours
I fine tuned Qwen3 0.6b for better Spanish understanding. Tenorio 0.6b
Have you tried deepseek harness?
Qwen3.8-27B vs Ornith-1.5-35B on the Bioluminescent Abyssal Temple challenge. Same prompt. Same Grok Build harness. 4-bi
The 21.7GB model is actually the faster local model. Ornith-1.5 comes in 9B, 35B-A3B, and 397B variants, but the 35B-A3B
Ornith-1.5-35B running on a 12GB RTX 3060 + 16GB RAM is pretty ridiculous. → 170K context → ~53 tok/s decode → ~900 tok/
The two recipes, exactly as they run on my Sparks right now: Spark 1, Qwen 3.8 27B dense, SGLang + DSpark speculative: -