Llama

Llama is no longer just Meta's open-weight flagship—it has become the runtime dependency for a growing ecosystem. The graph shows Llama is used by Ollama-OCR, PaperDebugger, Kiro, and LLMFit, while it itself depends on Mistral, Gemma 4, DeepSeek V4, and Qwen 3.6. This bidirectional flow reveals a strategic shift: Llama is both a competitor to and a consumer of rival models. Recent news confirms Ollama now runs DeepSeek V4, Gemma 4, and Qwen 3.6 locally, suggesting Llama's architecture is being absorbed into a broader inference layer. Microsoft's partnership adds enterprise distribution heft. But the tension is clear—Llama's open-weight nature lets others build on it, yet it also enables competitors' models to run on the same infrastructure. The question is whether Meta can monetize this ubiquity before Llama becomes a commodity substrate.

• ·Llama is used by 4 downstream products (Ollama-OCR, PaperDebugger, Kiro, LLMFit) while itself using 4 rival models
• ·Ollama now supports DeepSeek V4, Gemma 4, and Qwen 3.6 locally, indicating Llama's architecture is being leveraged as a cross-model runtime
• ·Microsoft partnership provides enterprise distribution channel for Llama-based solutions
• ·Recent Nature study shows all major AI models including Llama can be manipulated into academic fraud
• ·Llama's open-weight strategy creates both adoption and commoditization risk