hwkim-dev/llm-lite

llm-lite enters an extremely crowded market of inference engines dominated by giants like llama.cpp and MLC LLM. With 0 stars and 0 days of age, it is currently categorized as a personal experiment or early-stage prototype. The primary technical differentiator is the claimed 'custom FPGA NPU backend,' which is a high-effort engineering task compared to standard CPU/GPU implementations. However, the lack of community traction makes it highly vulnerable. Frontier labs (OpenAI/Anthropic) are unlikely to build FPGA-specific edge backends, but Meta's ExecuTorch and the Apache TVM ecosystem represent massive, well-funded competition in the same niche. The defensibility is low because, while FPGA work is difficult, the lack of an ecosystem or 'data gravity' means users have no switching costs and will likely prefer more established, audited frameworks. Platform domination risk is medium; while big clouds don't care about niche FPGAs, hardware vendors like Qualcomm or AMD could easily sherpa these capabilities into their official SDKs.