# RightNow-AI/picolm

Run a 1-billion parameter LLM on a $10 board with 256MB RAM

Repository: https://github.com/RightNow-AI/picolm
Canonical: https://ross.abutalabs.com/products/picolm
Homepage: https://www.rightnowai.co/forge
Language: C
License: MIT
License Family: permissive
Topics: arm, embedded, inference, llm, openclaw, picoclaw, quantization, raspberry-pi, risc-v
Last push: 2026-02-22T22:05:30+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 68, release rhythm 35, longevity 14
- inputs: {"age_days": 196, "days_push": 192, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1921, forks 240 (observed 2026-08-28T04:05:55.236132+00:00)

## What it is
PicoLM is a pure C11 LLM inference engine that runs 1-billion parameter models in GGUF format on extremely constrained hardware like $10 boards with 256MB RAM. It compiles to a single ~80KB binary with zero dependencies, uses ~45MB of runtime RAM, and supports JSON grammar mode for reliable structured output.

## Use cases
- run an LLM locally on a raspberry pi or cheap SBC
- offline chatbot inference without cloud APIs
- pipe prompts to an LLM from a shell script or agent
- serve as the local brain for an embedded AI assistant
- generate valid JSON tool calls from a small 1B model
- run LLM inference on 256MB RAM devices
- build a fully offline AI agent with no API keys

## When to choose
- you need LLM inference on extremely constrained embedded hardware
- you want a zero-dependency, single-binary, no-Python solution
- privacy and offline operation are requirements
- you're building an agent like PicoClaw that spawns an LLM subprocess

## When to avoid
- you need large models (7B+) or GPU acceleration
- you want a full-featured inference server with OpenAI-compatible APIs
- you need broad model architecture support or frequent cutting-edge features
- you prefer Python tooling and ecosystem integration

## Facets
- artifact type: library
- maturity: active
- function: llm-inference, cli, machine-learning
- domain: large-language-models, embedded-systems, artificial-intelligence
- platform: embedded, cli, cross-platform
- tags: gguf, quantization, edge-ai, offline-inference, zero-dependencies, risc-v, raspberry-pi, single-binary, low-resource, command-line, linux, arm

## Member repositories
- RightNow-AI/picolm (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:55.236132+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:09:40.873554+00:00, confidence not recorded.
  - readme: https://github.com/RightNow-AI/picolm (fetched 2026-08-28T04:05:55.236132+00:00, sha a48685b3f939)
  - homepage: https://www.rightnowai.co/forge (fetched 2026-08-29T10:49:05.187155+00:00, sha 623f618c156c)
  - site_page: https://www.rightnowai.co/docs (fetched 2026-08-29T10:49:05.199622+00:00, sha 66a23f9f0969)
  - site_page: https://www.rightnowai.co/changelog (fetched 2026-08-29T10:49:05.197099+00:00, sha 1532de1807ce)
  - site_page: https://www.rightnowai.co/pricing (fetched 2026-08-29T10:49:05.201669+00:00, sha 93aa309811e4)
- Data as of 2026-08-30T08:39:29.467469+00:00.