Your AI is always ready: like your own car versus a taxi. No queues, no request limits.
Your data never leaves your hardware. Period.
No subscription, no per-request cost. Once you have the hardware, the AI is yours.
Block introduces Dessa, a speech model trained from scratch on human conversation. The company already uses it for phone support: a case study in how natural a TTS/STT is when not fine-tuned on a large existing model.
AMD merges its ZenDNN fork into oneDNN and joins the UXL board; Intel brings ACE extensions for matrix multiplication. oneDNN becomes the default x86 library in PyTorch: one software path to optimize for local AI on CPU, regardless of vendor.
A near-frontier mobile agent is published with fully open training environments and recipes: MobileGym++ simulates 35 commercial apps with cross-app workflows, with controllable reset and verification. Relevant for anyone building on-device agents.
LLMspeed is an open-source tool (Hacker News showcase) that estimates inference speed for 250+ model/hardware combinations: RTX 3060 to H100, Mac Studio M5 Ultra, CPU-only. Measured estimates come from community benchmarks (r/LocalLLaMA, localscore.ai); the rest are derived from similar setups or projected from specs. Useful for choosing hardware before spending.
MLExpert raises the bar to 35B and a 72.7% medal rate; EpochGo runs three AutoML agents on 16 GB at zero API cost: what works today, the math and the limits.
AMD is merging ZenDNN into oneDNN and Intel is folding in its ACE extensions: one open-source x86 acceleration stack in PyTorch, already the default. What it changes if you run models locally on CPUs.
NVIDIA re-released Nemotron Parse (v1.1, v1.2, 2.0) in the same week, on October 20: under 1 billion parameters, page structure included. Local setup with vLLM and the honest limits.
An online guide matching open-weights models to real hardware: for every GPU, Mac and mini-PC it shows what runs, how much memory it needs and at how many tokens per second, with guides on quantization and memory. The right tool for the own-hardware path.
Mixture-of-Experts model: 30B total params but only 3.3B active per token. Small-model speed with mid-size quality; the best speed/quality ratio in its class.
Il modello MoE della famiglia Gemma 4: 25,2B di parametri totali con 3,8B attivi per token. Contesto 256K, input testo+immagini, oltre 140 lingue, Apache 2.0. In Q4_K_M pesa circa 16,8GB: la scelta per chi ha 24GB di VRAM e vuole qualità da modello grande con velocità da modello piccolo.
Open source tool to run LLMs locally with simplicity.
Meta's largest open model: closed-frontier quality but datacenter requirements. In practice it needs high-VRAM multi-GPU or the cloud.
Home.setup_preview_cta_desc
A free, step-by-step PDF: hardware, models, tools and first steps to bring AI into your business — no cloud, no data sharing.
Download the free guideFree. No strings attached. No email address required.
The newsletter is free and only needs your email. With a full account, the site works for you: save your setups, personalize your profile, and keep everything in control.
Save your hardware and model plans: find them all, ready, when you come back.
Name, bio and preferences: your space on the site, yours forever.
Easy access to the full newsletter archive and the weekly selection of what's changing in local AI.
Manage email, password and subscription whenever you like. No commitments, no spam.
