DumbleD

I took a photo of a bowl on my desk. 📸 55 seconds later: a tileable, game-ready 3D material, path traced live in the browser, straight into Godot and Blender. Built with Meta’s Muse Spark, SAM 3.1 & Muse Image for the Meta Global AI Hackathon. youtu.be/lK5YGg_GBmg

TensorFold engine 1.2.1 is out. A few highlights: • MiniMax-M3.1-Flash on CUDA across two GB10s, using Mia-AiLab's EXL3 pack. • Gemma 4 31B on GB10 with Google's MTP assistant: 28.3 tok/s versus 12.2 undrafted, at one stream. • Qwen3.5/3.6 windows of 1,048,576 tokens on CUDA with YaRN. • MOSS-Image end to end on Metal, via the CLI. • Gemma 4 E4B prompt processing 2 to 2.6 times faster on Intel Arc Pro B70. There are also opt-in GLM long-conversation improvements, server load reporting, Living Weights bulk fact deletion and fixes for lesson failures and Flash Next's long-prompt slowdown on GB10. Huge thanks to everyone who contributed, tested and reported issues: @andersjw_ @atasrb @Bizuayeu @grantoverton @ivanfioravanti @jayleaton @niklaslenz_ai @2Halves1brain @wesley4546 And on GitHub: AbrahamCete, akol1, BlivionIaG, BobClawblaw, gilby, Jenpo, jschmied, kieranscotney, laudney, Mirrdhyn, ppardus, russellmm and ThomasHalwax. Upstream fix credits: florianbrede-ayet and mikolaj92. Thank you for the time, code, testing and hardware access you keep sharing with TensorFold. Apologies if I've missed anyone. @petruspennanen @plotarmordev @MiaAI_lab @volatilemarkts tensorfold.dev

TensorFold engine 1.2.0 is out 🙌 Qwen3.8-27B with MLX 4-bit weights and DFlash2, compared with 1.1.9: • M3 Ultra: 14.4% faster decoding with one stream, 44.2% higher combined throughput with 16. • M5 Ultra: 5.8% faster decoding with one stream, 25.2% higher combined throughput with 16. New support includes: • MiniMax-M3.1-Flash on Metal. • NVFP4 Qwen3.6-35B-A3B and dense EXL3 Qwen3.8-27B on CUDA. • Gemma 4 E4B on Intel Arc Pro B70. The measured speedups kept matching tokens. Qwen-VL image replies can differ from 1.1.9 because image preparation now matches the checkpoint's own processor. Huge thanks to everyone credited in this release: @bhcc2025 @Bizuayeu @diwakergupta @jvr0x @jayleaton @joshuaswarren @woolsey @petruspennanen @volatilemarkts @MiaAI_lab @plotarmordev And on GitHub: AbrahamCete, akol1, alexderz, Arminova, BadAd84, BlivionIaG, BobClawblaw, fche, gilby, jrb-ai, jschmied, laudney, Mirrdhyn, mrv777, ppardus, simon-lin88, ThomasHalwax, valacroix and wojo. Thank you for the PRs, reviews, testing, bug reports and hardware access. The time and knowledge you keep sharing with TensorFold mean a lot to me. Apologies if I've missed anyone. tensorfold.dev