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:

• llama.cpp project
• llama-server
• Ollama runtime FAQ
• Ollama OpenAI compatibility

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?