llama
4 stories filed under llama on Beyond Market Intelligence. The newest of them: “Smaller model, sharper reasoning: 44M parameters trained from scratch on CPU”, “Nvidia's simple math cuts costly recomputation across AI models”, and “Meta enters the AI coding arena with agent tools and smarter models.”. Three weeks ago, SHADOW-250M proved a 60 MB model could retrieve records from disk. Swapping models mid-session has always meant paying the full prefill tax again, until now. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every llama story on Beyond Market Intelligence, newest first.
Smaller model, sharper reasoning: 44M parameters trained from scratch on CPU
Three weeks ago, SHADOW-250M proved a 60 MB model could retrieve records from disk. Now, SHADOW-50M pushes the same idea further: 44M parameters, 19.8 MB, and roughly 1,900 tokens per second on a laptop CPU. It reasons over what it retrieves, runs arithmetic through a fixed circuit, and answers from memory without re-reading source text. It loses to larger models on standard benchmarks, and the author says so plainly.
Nvidia's simple math cuts costly recomputation across AI models
Swapping models mid-session has always meant paying the full prefill tax again, until now. Nvidia's researchers found that a simple linear mapping can transfer a KV cache between compatible models, bypassing the expensive recomputation that bogs down multi-LLM agentic workflows. The math is refreshingly direct, not a heavyweight neural network. On tested pairs, this approach runs up to 25 times faster while keeping nearly all accuracy. It's a practical fix for a costly bottleneck, and it points toward leaner long-horizon AI systems.
Meta enters the AI coding arena with agent tools and smarter models.
Meta is entering the AI coding race with intent. Today's beta release of Muse Code, a terminal-based agent built for large repositories, pairs with Muse Spark 1.2, a model co-trained alongside its own harness. The pitch is practical: persistent background agents, parallel worktrees, and a replay-exact audit log. Pricing undercuts rivals significantly, though the contributor tier trades tokens for training data. It's a calculated move from a company that once championed open weights. For now, the frontier just got more crowded.
Unlock faster local coding workflows with AI and speculative decoding
Local AI has a new ceiling. With Qwythos-9B-Claude-Mythos-5-1M running through llama.cpp, you can pair speculative decoding with the Pi coding agent to build fast, private workflows right on your own hardware. The OpenAI-compatible API keeps integration straightforward. This isn't about hype; it's about practical leverage. If you're still wrestling with cloud dependencies, this setup offers a refreshing alternative. And if you're questioning how much to trust these tools, our piece on talking to an AI clone is worth a read.