LLM Inference.
Everywhere.
From bare-metal unikernels to mobile devices. Run large language models locally, in any language, on any hardware. No cloud required.
The Full Inference Stack
Every layer of LLM inference, covered by purpose-built open-source tools. From silicon to smartphone.
Projects
Five tools, one mission: make LLM inference accessible everywhere.
mullama
Python / RustRun any LLM locally. Use it from any language. Deploy anywhere. Drop-in Ollama replacement with native bindings for Python, Node.js, Go, PHP, Rust, and C/C++.
llamafu
DartRun AI models directly on mobile devices. Flutter FFI plugin for on-device inference with vision, tool calling, and streaming support.
unillm
RustA modular LLM inference runtime written in Rust. 47 model architectures, unified interface, type-safe and composable.
cllm
CA bare-metal C unikernel for serving LLMs. No OS, no overhead. Boots directly on hardware and serves inference over HTTP.
zigllm
ZigLearn how LLMs work by building one in Zig. 18 model families, 285+ tests, progressive architecture from tensors to text generation.
Why Local Inference?
Cloud APIs have their place. But local LLM inference unlocks capabilities that cloud can't match.
Complete Privacy
Data never leaves your infrastructure. Zero third-party exposure.
Zero Latency
No network round trips. Sub-millisecond inference for real-time applications.
No Per-Token Cost
One-time compute cost. No API bills, no rate limits, no vendor lock-in.
Full Control
Choose your model, quantization, hardware, and deployment strategy.
For Developers
Embed LLMs directly in Python, Rust, Dart, Go, PHP, Node.js, C, or Zig. No server required.
For Architects
Deploy LLM inference at every layer of your stack. From edge devices to bare-metal servers.
For Investors
We're building the infrastructure layer for local AI. Five tools covering the full inference stack.