LocalClaw/LLM/Ornith-1.5-9B
Open-weight local LLM

Ornith-1.5-9B

Catalogue summary: Official MIT reasoning model from Ornith AI with a 262K native context window, tool-calling focus, and official Q4_K_M GGUF, MLX and Ollama/llama.cpp paths for local coding-agent experiments on 16GB+ machines.

Repository editorial metadata; verify comparative claims in the linked upstream material.

16 GB catalogue minimum16 GB RAMQ4_K_MCoding assistant
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Parameters
9B
Minimum RAM
16 GB
Model size
5.63 GB
Quantization
Q4_K_M

Can Ornith-1.5-9B run locally?

Ornith-1.5-9B has a catalogue minimum of 16 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use ornith-1.5-9b as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

Model sourcePublic GGUF repository (verified 2026-09-07): ornith-ai/Ornith-1.5-9B-GGUFUpstream releasePaper or model cardLicence text
chatcodereasoningagenticlong-contextgeneral

Install path

01
Check RAM fitMinimum 16 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch ornith-1.5-9b in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: ornith
  • Parameters: 9B
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 16 GB
  • Catalogue model size: 5.63 GB
  • Tags: chat, code, reasoning, agentic, long-context, general

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Catalogue tags

  • chat
  • code
  • reasoning
  • agentic
  • long-context
  • general

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

speed
7
quality
8
coding
9
reasoning
8

Technical notes

Developer
Ornith AI
License
MIT
Context window
262,144 tokens
Architecture
Dense 9B Ornith-1.5 reasoning model developed from the Ornith self-improvement line, with Qwen-style reasoning/tool-call parsing, native 262K context and optional YaRN scaling for very long workloads.

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Starter desktopMac mini M4 16GBPortable fitMacBook Air M4 16GBBest 16GB models16GB RAM guide

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

Ornith-1.5-35B-A3B 35B (3B active, MoE)Ling-3.0-tiny 7.9B (1.3B active, MoE)Nemotron 3.5 Lightning 30B-A3B 30B (3B active, MoE)Bonsai 27B 27.3B (ternary / 1-bit)Spark-X2.5-4B 4BAgents-A1 4B 4BK2-Horizon-0.9B 0.9BLFM2.5-2.6B 2.7B

Where to go next