AI Large Language Models

colibri

A dependency-free C engine that streams expert weights from disk to run the 744B-parameter GLM-5.2 MoE model on consumer hardware with as little as 25 GB of RAM.

What is colibri?

colibri is a lightweight, pure-C inference engine for the GLM-5.2 744B-parameter Mixture-of-Experts model. It streams expert weights from disk and requires no Python, GPU, or external dependencies at runtime, enabling local execution on a machine with ~25 GB RAM.

colibri vs Similar AI Tools

Pricing ModelFreeFree, FreemiumPaidPaid
Free Credits
Key Features
  • Pure C implementation with zero external dependencies
  • Streaming of expert weights from disk, with LRU cache and optional pinned hot-store
  • Faithful GLM-5.2 (glm_moe_dsa) forward pass, validated token-exact
  • Access to multiple AI models (GPT, Claude, Gemini, DeepSeek, Grok, etc.)
  • Team collaboration in private workspaces
  • File upload support (PDF, code, docs) with contextual understanding
  • Single model for all tasks with no mode switching
  • OpenAI-compatible API
  • Claude Fable-level quality on evaluated tasks
  • Unlimited tokens
  • Unlimited context window
  • Flat monthly pricing
Pros
  • Runs a 744B-parameter model on consumer hardware with only 25 GB RAM
  • Open source and fully transparent (C code, no dependencies)
  • Access to multiple leading AI models in one platform
  • Built-in team collaboration features
  • High quality comparable to Claude Fable
  • Significantly lower cost than frontier models
  • Unlimited tokens and context
  • Flat predictable pricing
Cons
  • Very slow cold decode (0.05-0.1 tok/s on typical NVMe)
  • Requires ~370 GB disk space for the converted int4 model
  • Limited messages and credits on the free plan
  • Advanced features require paid subscription
  • Newer model with limited independent validation
  • Exact pricing not publicly detailed
  • High monthly cost ($10K+)
  • Requires 14-day provisioning
Best For
  • Developers wanting to run massive models locally
  • AI researchers exploring MoE architectures on limited hardware
  • Teams needing diverse AI model access
  • Content creators and researchers
  • Developers seeking high-quality LLM at lower cost
  • Teams needing a single versatile model
  • Enterprise teams running AI agents at scale
  • Software engineering teams needing long-horizon code generation

How to use colibri?

  1. 1Clone the repository: git clone https://github.com/JustVugg/colibri.git
  2. 2Build the engine: cd c && make
  3. 3Convert the FP8 model to int4: ./coli convert --model /path/to/model
  4. 4Run chat: COLI_MODEL=/path/to/model ./coli chat
  5. 5(Optional) Use the web UI or OpenAI-compatible server for a graphical interface.

colibri Key Features

  • Pure C implementation with zero external dependencies
  • Streaming of expert weights from disk, with LRU cache and optional pinned hot-store
  • Faithful GLM-5.2 (glm_moe_dsa) forward pass, validated token-exact
  • MLA attention with compressed KV-cache (57x smaller)
  • Native MTP speculative decoding for up to 2.8 tokens per forward
  • Integer-dot kernels (int8/int4) with AVX2 acceleration
  • Online learning cache that adapts to usage patterns

colibri Use Cases

  • Running a 744B-parameter MoE model on a consumer laptop or desktop
  • Offline chat and inference without cloud dependencies
  • Research and experimentation with large MoE architectures
  • Educational demonstrations of frontier model capabilities on limited hardware

colibri Pricing & Free Credits

colibri currently operates on a Free model.

This tool is completely free to use

Open Source

Free

Apache 2.0 license. No cost for use or modification.

colibri Pros & Cons

Pros

  • Runs a 744B-parameter model on consumer hardware with only 25 GB RAM
  • Open source and fully transparent (C code, no dependencies)
  • Efficient disk streaming with caching enables long-running usage
  • Active community benchmarks and improvements

Cons

  • Very slow cold decode (0.05-0.1 tok/s on typical NVMe)
  • Requires ~370 GB disk space for the converted int4 model
  • Only supports GLM-5.2 model (no multi-model flexibility)
  • CUDA backend is experimental and limited

What is colibri best for?

  • Developers wanting to run massive models locally
  • AI researchers exploring MoE architectures on limited hardware
  • Enthusiasts interested in frontier model inference without cloud costs

colibri FAQ

Top free alternatives to colibri

Opper AI logo

A unified AI gateway providing access to 300+ leading models through one EU-hosted, GDPR-compliant API with an OpenAI SDK-compatible interface.

Free
discode.ai logo

One chat interface that gives you access to over 100 AI models, automatically selecting the best model for your task while tracking energy usage and protecting your privacy.

Free
Oxlo.ai logo

Oxlo.ai is a privacy-first AI inference API offering request-based pricing for over 45 open-source models.

Free
Graphsignal logo

Graphsignal is a production-scale inference profiling platform that helps engineers optimize AI performance across models, engines, GPUs, and other accelerators.

Free

Best alternatives AI Tools to colibri

Aymo AI logo

All-in-one AI platform for teams providing access to leading AI models like GPT, Claude, Gemini, and more in a secure collaborative workspace.

EB

Echo is an open-weight AI model offering Claude-class performance at one-third the cost, adaptable to chat, code, and agent tasks.

L

LiquidBrain.ai offers unlimited token and context AI inference on a fixed monthly bill, using a patented distributed inference engine for private, scalable model deployment.

DwarfStar logo

A specialized local inference engine for large language models, optimized for Apple Silicon and SSD streaming on memory-constrained systems.

LibArgus logo

Unified, zero-allocation native AI inference runtime for Java, consolidating LLM, vision, and speech pipelines via Project Panama.

Opper AI logo

A unified AI gateway providing access to 300+ leading models through one EU-hosted, GDPR-compliant API with an OpenAI SDK-compatible interface.

Free
Auriko logo

A unified API platform for LLM inference with cost optimization, routing, observability, and automatic failover.