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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.
The most accessible consumer GPU with 12GB VRAM: the entry point for local inference. Comfortably runs 7-8B quantized models.
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.
Il modello MoE grande della famiglia Llama 4 di Meta: 400B di parametri totali con 17B attivi (128 esperti). Contesto 1M di token, nativamente multimodale (testo+immagini), licenza comunitaria Meta. Richiede multi-GPU o macchine con memoria unificata 128GB+ anche quantizzato.
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