Flash-MoE: Running a 397B Parameter Model on a Laptop - companion paper with the full details: github.com/danveloper/f... #localinference #ai #llms #ml #macbook #apple

GitHub - danveloper/flash-moe: Running a big model on a small laptop Running a big model on a small laptop. Contribute to danveloper/flash-moe development by creating an account on GitHub.

Very interesting: "Pure C/Metal inference engine that runs Qwen3.5-397B-A17B (a 397 billion parameter Mixture-of-Experts model) on a MacBook Pro with 48GB RAM at 4.4+ tokens/second with production-quality output including tool calling." #localinference #llms #ai #ml #macs

github.com/danveloper/f...

Just ran setup_env.py and it compiled the BitNet‑b1.58‑2B‑4T C++ backend with CMake in seconds. Ready for local inference on your machine—no Hugging Face hassle. Dive into the details! #BitNet #PythonCMake #LocalInference

🔗 aidailypost.com/news/python-...

Image generation quality has exploded, but running it locally is still messy — especially for Java developers and ARM hardware.

A hands-on guide on embedding a native image model directly into the JVM using #Quarkus and the #Java #FFM API.

buff.ly/9t29far

#LocalInference #AIEngineering

Unlocking Edge AI: How A Hybrid Data Architecture Can Power Local LLM Deployments One of the great things about a hybrid unstructured data architecture is that data can be pinned to the edge in a particular location, even when people are working on that dataset on their edge nodes ...

Unlocking Edge AI: How A Hybrid Data Architecture Can Power Local LLM Deployments

www.linkedin.com/pulse/unlock...

#AI #EdgeAI #EnterpriseAI #LocalLLM #Ollama #OpenWebUI #LocalInference #UnifiedDataManagement #UnstructuredData #UDM