Open-source engine for faster local language model inference with bit-for-bit identical output
Stage
Not given
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Founded
2023
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Based in
DE
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Pricing model
Freemium
Open source
Pricing
Free tier: 1,000 credits per day, resets every 24 hours. Pay-as-you-do: €10 per 1,000 credits. Enterprise: Custom pricing with volume commit, on-prem, and SLAs.
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Key features
Compile firmware against production toolchains remotely without installation
Execute firmware on virtual chip instances with verdict, trace, and register state
Unit and integration testing on virtual hardware with coverage reports
Inject synthetic sensor data into running firmware
Query collective embedded corpus for chip-specific solutions
Support for Rust, C, and multiple RTOS frameworks
Speculative decoding for faster token generation
Prompt caching for context reuse
Real-time prompt processing while typing
Concurrent request batching
Bit-for-bit identical output verification
Multi-GPU and multi-backend support (CUDA, Vulkan)
Vision model support with image encoding
Mixture-of-experts model support
RAD-1 automatic draft token generation
Cross-platform support for Windows and Linux
What makes it different
Virtual instances of actual target chips, not generic CPU emulation, with same binary, peripherals, and interrupt timing as physical boards
Shared corpus that deepens with every call, allowing agents to inherit knowledge from previous runs
Eliminates need for physical hardware for testing, porting, and debugging
Single MCP endpoint exposes cross-compilation, peripheral simulation, sensor IO, and chip-specific plumbing
Access to licensed toolchains on demand without local installation
Exact deterministic inference: all default optimizations preserve bit-for-bit identical output compared to plain decoding
4.5× faster performance on editing tasks on small models compared to llama.cpp
Verifiable reproducibility through receipt system that records all request parameters
Nine optimization techniques enabled by default with ability to disable each individually
No dependency on draft models or external selections for speculative decoding
Chiplab
Kinetic