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LOCAL LLM
All blog posts, tools, and guides about Local LLM from Developers Digest.
7 resources - 7 posts
Blog Posts
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LFM2.5-2.6B: Liquid AI's On-Device Agent Model Runs at 220 Tokens/s in Under 2.5 GB
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.
7 min read
TurboFieldfare: Running Gemma 4 26B in 2 GB of RAM on Any M-Series Mac
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.
8 min read
Jamesob's Guide to Running SOTA LLMs Locally: The Hardware and Config That Actually Works
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.
9 min read
Local Qwen Is a Different Tool, Not a Worse Opus
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.
7 min read
Cohere's North Mini Code: A 30B Open-Weight Coding Model That Runs on One H100
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.
7 min read
The Best Local Coding LLMs in 2026: Run Enterprise-Grade AI Without the Cloud
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.
8 min read
Gemma 4: The Open Model Guide for Developers
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.
11 min read
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