Technical overview: Lizard Native and Caterpillar vs llama.cpp and Ollama
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The new architecture overview explains why these four names are not interchangeable categories.
What each layer is for
- Lizard Native — a specialized Windows-first resident-GPU provider using Direct3D 12 for its supported native subset.
- Caterpillar — a standalone clean-room provider built around a typed DAG, reused activation arena, and compiled execution plan.
- llama.cpp — a broad cross-platform inference toolkit and server with many CPU and GPU backends, wide model coverage, continuous batching, embeddings, reranking, and multimodal features.
- Ollama — model packaging, acquisition, lifecycle, scheduling, and local APIs, including configurable model keep-alive and OpenAI-compatible endpoints.
Lizard's extra control-plane work is the hardware scan, exact GGUF fit decision, native-provider routing, and provider-separated evidence. llama.cpp and Ollama remain useful stock baselines and compatibility fallbacks. No provider wins every model, metric, or machine.
Primary baseline documentation:
Read the complete comparison table: https://lizard-llm.qendryx.com/technical-overview.html
Question: Which comparison dimension matters most to you: platform coverage, model lifecycle, memory fit, or provider-level benchmark evidence?
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