DeepSeek V4-Pro, the strongest open-weight value model (1.6T/49B active), was promoted to general availability with MIT weights on Hugging Face; community GGUF quants appeared within hours.
Meta's first Muse-family open-weights release: a ~30B dense multimodal agentic model whose 4-bit quant sits under 20 GB, shipping BF16, GGUF and ExecuTorch builds for a 24/32 GB VRAM envelope.
Alibaba's first Max-class model released as open weights: a 2.4T MoE / A95B FP8 checkpoint, text-only under a revenue-share license and aimed at rack-scale deployment.
Formulates quantization as latency-performance maximization under an allowable generation-quality degradation budget for Gemma-3-1B, directly targeting efficient local inference on small models.
A decay-aware mixed-precision scheme for the fixed-size recurrent states in Gated DeltaNet and Kimi Delta Attention layers, cutting inference memory that would otherwise grow with sequence length.
Improves on-policy self-distillation by adapting the teacher distribution toward the student based on a verifier, refining distillation for small models trained locally on their own rollouts.
Shows post-training quantization can trigger backdoors when the full-precision source is evaluated but quantization is applied downstream, a security risk for small-model edge deployment.
Eliminates a device-to-host round trip and a second graph build per token for encoder-based vision inputs, released with WebGPU and Vulkan tuning builds (b10713/b10714).
Word documents and slide decks generated in the terminal now render as finished documents with a clickable page-strip of numbered thumbnails, and the 'preview may differ' warning is removed.
A tree-sitter based code-context engine that serves coding agents structural answers about a codebase within a token budget, exposing an MCP server to keep code analysis local.
A r/LocalLLaMA thread on GLM-5's 1.5TB BF16 size examines why pooled consumer-GPU inference (Petals, Gensyn, llama.cpp RPC) hasn't become mainstream, citing node-level privacy, WAN latency and marginal speedups.
A self-hoster's community node wires the Pollinations AI chat model into n8n's AI Agent and Chain nodes, giving users a free path to experiment with AI workflows before paying a provider.
Free means $0 per token today, not unlimited or unconditional: both platforms rate-limit free use and require a free API key, some OpenRouter free endpoints may train on your prompts unless you opt out in account settings, and the roster changes daily. Verified against each provider's own pricing data at generation time — but pricing changes without notice, so confirm it's still free on the provider's own page before you rely on it.
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