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Nunchux AI Introduces VC-Attention: A Training-Free Low-Bit Attention Kernel That Speeds Up Video Diffusion Transformers

Nunchux AI introduces VC-Attention, a training-free low-bit attention kernel that speeds up video diffusion transformers up to 3.58x.

Nunchux AI unveiled VC-Attention, a training-free attention kernel for video Diffusion Transformers combining V-Smooth (k-means value-token grouping with block-mean residual quantization) and ExpCast-FP8 (single multiply-add softmax exponentiation). Benchmarks on Wan2.2-T2V-A14B, LongCat-Video, HunyuanVideo-1.5, and MiniMax-H3 show 1.59x attention speedup on B200 at 8-bit and 3.58x on RTX 5090 at 4-bit, with end-to-end gains up to 1.70x. It beats SageAttention2 by 2.3 dB PSNR on Wan2.2 at 8-bit and SageAttention3 by up to 3.6 dB at 4-bit. No public kernel release yet; a proprietary extension runs in Nunchux's stack.

MarkTechPost · 8h agoAI research 2 sources1

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)

Latent Space AI news roundup: Steve Yegge shuts down Gas Town, Databricks reports 60% higher coding spend on GPT-6 Astra, OpenAI launches misalignment disclosure framework.

Latent Space's AI News digest for September 15-16, 2026 leads with Steve Yegge shutting down his Gas Town orchestrator despite spending thousands monthly on coding-agent subscriptions. Databricks rolled out GPT-6 Astra to roughly 3,500 engineers, reporting superior long-horizon performance over Opus 5 and Sol 5.6 but a ~60% increase in coding spend. OpenAI published a formal framework for disclosing model misalignment incidents with six case reports, while Microsoft and Google Research released safety papers on 'capability laundering' and the Fuse motive-inference benchmark. Xiaomi shared live RL training telemetry for MiMo-V2.6, estimated at $493k/day for the 1T-class Pro run.

Latent Space · 1h agoAI industry 12 sources

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPost · 1d agoAI tools & infra2

Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.

Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.

MarkTechPost · 1d agoModel release