LOCAL LLM

Liquid AI released LFM2.5-VL-3B on August 12, 2026: a 3.1B open-weights vision-language model that averages 80.7 on ScreenSpot-v2, doubles ToolSandbox to 59.5, and decodes at 228 tokens/s on an M5 Max in about 3 GB of memory. Here is what shipped, the benchmark caveats, and how to run it.

Liquid AI shipped LFM2.5-2.6B on August 4, 2026: a 2.6B open-weight model trained for agentic work inside real harnesses, decoding at 220 tokens/s on an M5 Max and 113 tokens/s on a Ryzen CPU. Here is how it was trained, what the benchmarks say, and how to run it.

TurboFieldfare is a custom Swift and Metal inference engine that runs Google's 26B-parameter Gemma 4 MoE model in roughly 2 GB of RAM on any Apple Silicon Mac, including 8 GB base models.

A fair, sourced comparison of the four runtimes developers reach for when they want a coding agent talking to a model on their own hardware instead of an API: Ollama's convenience, LM Studio's GUI, vLLM's throughput, and llama.cpp's control. What each is actually for, and which to pick.

A detailed breakdown of jamesob's viral local LLM guide covering the $2k and $40k hardware paths, critical BIOS settings, and why most setups fail at PCIe negotiation and IOMMU.

Alex Ellis shares real production experience running local LLMs: $12k hardware investment, 2-3 month ROI, and why treating local models as Opus substitutes misses the point entirely.

Cohere shipped its first developer-facing model on June 9, 2026. North Mini Code is a 30B mixture-of-experts coding model with 3B active parameters, Apache 2.0 weights, and a deployment footprint of a single H100. Here is what it actually offers and where the open questions are.

Choosing a local coding LLM in 2026 means balancing benchmark performance, hardware cost, and the compliance pressure to keep code off third-party servers. Here is what to run and on what hardware.

Gemma 4 ships byte-for-byte open weights from Google DeepMind. How developers deploy it locally, fine-tune it, and ship agents on top of it.