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Ornith 1.5 9B
◆ OPEN
by Ornith AI
OPEN WEIGHTSLOCALCHINA-MADE
RANKED #194 OF 282 ASSISTANTS · OVERALL #308 OF 6,622 · VIBE SCORE 5.6 · 26 VOICES
Ornith-1.5-9B-MLX-4bit on Hugging Face (text generation). 3,043 downloads. Open weights for local or hosted use.
HAPPY10.0
4 mentions
SAD-
0 mentions
VIBE TRAJECTORY
26 weeks · 44 voices
AGING WELL
The crowd is warmer now than it was at the start.
HOW TO USE IT
Open weights. You can use a hosted service, or download it and run it yourself, free.
RUN IT ON YOUR OWN MACHINE
Open weights: download Ornith 1.5 9B once and it's yours. No subscription, no rate limits, works offline.
9B parameters, a Q4 file is about 5.5 GB (around 18 GB in BF16)
- BARE MINIMUM
Nvidia GeForce RTX 3060 12GB
12 GB VRAM
~$250 USEDQ4_K / Q5_K, ~35 tok/s, 8k context
- SWEET SPOT
Apple Mac mini M4 (16GB RAM)
16 GB UNIFIED MEMORY
~$599 NEWQ8_0 / Q6_K, ~40 tok/s, 16k context
- FULL POWER
Nvidia GeForce RTX 4090 24GB
24 GB VRAM
~$1750 USEDBF16 unquantized, ~110 tok/s, 32k context
LEARN HOW TO SET IT UP
WILL MY BOX RUN ORNITH 1.5 9B?
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.
WHAT CAN YOU ACTUALLY DO WITH IT?
Everyday jobs on each of those rigs: how long Ornith 1.5 9B takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNvidia GeForce RTX 3060 12GB | SWEET SPOTApple Mac mini M4 (16GB RAM) | FULL POWERNvidia GeForce RTX 4090 24GB |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~14 S | ~13 S | ~5 S |
| Summarize a documenta long report boiled down to the points that matter | ~43 S | ~38 S | ~14 S |
| Build a websitea small landing page, markup and styles together | ~3.8 MIN★★★★★ | ~3.3 MIN★★★★★ | ~73 S★★★★★ |
| Build a backendan API with routes, storage and tests | ~12 MIN★★★★★ | ~10 MIN★★★★★ | ~3.8 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~7.1 MIN★★★★★ | ~6.3 MIN★★★★★ | ~2.3 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.
WHAT THE INTERNET SAYS
Not enough discussion about Ornith 1.5 9B yet to call it. We found 26 posts but people did not say much either way.
21 POSTS · 5 COMMENTS · REDDIT · GITHUB · X · LEMMY
PEOPLE LOVE
- No fan mail yet. The crowd is still warming up.
PEOPLE COMPLAIN ABOUT
- No complaints yet. The internet is still looking for one. Suspicious.
LOOKING FOR AN ALTERNATIVE?
Other models the crowd has fully reviewed, starting with multimodal models like this one.
Grok 4.6
Qwen3.8 27B
GLM 5.3> SOURCES (12)
- REDDIT · 2026-08-19 · 452 UPVOTESWe have Q3.8 35B at home: 3x new Ornith 1.5 released
We have Q3.8 35B at home: 3x new Ornith 1.5 released
- REDDIT · 2026-08-19 · 68 UPVOTESOrnith 1.5: 9B dense and 35B/397B MoEs
Ornith 1.5: 9B dense and 35B/397B MoEs
- REDDIT · 2026-08-31 · 9 UPVOTESDoes anyone have real experience with Ornith-1.5-9B for coding
Does anyone have real experience with Ornith-1.5-9B for coding
- LEMMY · 2026-08-21 · 6 UPVOTESLow to midrange systems (8-32 GB) vs. free cloud tiers
Low to midrange systems (8-32 GB) vs. free cloud tiers
- REDDIT · 2026-08-27 · 3 UPVOTESComment in r/unsloth
Comment in r/unsloth
- GITHUB · 2026-08-21 · 2 UPVOTESOrnith 1.5 + Qwen 3.8 27B hybrid runtime + DFlash speculative decoding on Metal
Ornith 1.5 + Qwen 3.8 27B hybrid runtime + DFlash speculative decoding on Metal
- REDDIT · 2026-08-20 · 1 UPVOTESWhile Everyone Is Excited About Qwen 3.8 27B, Here’s the Reality for a 16GB AMD GPU User
While Everyone Is Excited About Qwen 3.8 27B, Here’s the Reality for a 16GB AMD GPU User
- GITHUB · 2026-08-21 · 0 UPVOTESEvaluate Ornith 1.5 and Qwen 3.6 on the dev-workflow matrix (prior rejections predate the current stack)
Evaluate Ornith 1.5 and Qwen 3.6 on the dev-workflow matrix (prior rejections predate the current stack)
- GITHUB · 2026-08-24 · 0 UPVOTESROCm decode is 2.4x behind llama.cpp on a DENSE model (Ornith-1.5-9B, qwen35): the gap is not MoE-specific
ROCm decode is 2.4x behind llama.cpp on a DENSE model (Ornith-1.5-9B, qwen35): the gap is not MoE-specific
- GITHUB · 2026-08-27 · 0 UPVOTESKERNEL-QUANT-CIQ-GEMM-ROCM: ROCm keep-quant GEMM (KQuantGemmK) has no MFMA arm — 61.5% of GPU time, ~4.4x behind llama.cpp mul_mat_q on…
KERNEL-QUANT-CIQ-GEMM-ROCM: ROCm keep-quant GEMM (KQuantGemmK) has no MFMA arm — 61.5% of GPU time, ~4.4x behind llama.cpp mul_mat_q on gfx1200
- GITHUB · 2026-09-01 · 0 UPVOTESfeat(mcp): Lightweight MCP Server with Palace Query Language (PQL) & Principled Retrieval Engine
feat(mcp): Lightweight MCP Server with Palace Query Language (PQL) & Principled Retrieval Engine
- X · 2026-08-24 · 0 UPVOTESModels have gotten so good, it doesn't really matter what you use. Stock Pi agent + good MCP + good prompt -> decent
Models have gotten so good, it doesn't really matter what you use. Stock Pi agent + good MCP + good prompt -> decent