NVIDIA AI Release VibeTensor | Mistral Launches Voxtral Transcribe 2

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VIBETENSOR is an Apache 2.0 open-source deep learning runtime whose implementation changes were generated by LLM coding agents under high-level human guidance. It implements a PyTorch-style eager stack with a C++20 tensor core, schema-lite dispatcher, reverse-mode autograd, CUDA streams and graphs, a stream-ordered caching allocator, and a versioned C plugin ABI, all exposed via a vibetensor.torch Python frontend and an experimental Node.js layer. The system was built over ~2 months using tool-driven validation, combining CTest, pytest, differential checks against PyTorch, allocator diagnostics, and long-horizon training regressions. AI-generated Triton and CuTeDSL kernels show up to ~5–6× microbenchmark speedups over PyTorch, but end-to-end training on small Transformers, CIFAR-10 ViT, and a miniGPT-style model is 1.7× to 6.2× slower, highlighting the “Frankenstein” effect where locally correct components compose into a globally suboptimal yet informative research prototype......… Read the full analysis/article here.

Mistral’s Voxtral Transcribe 2 family introduces 2 complementary speech models for production workloads across 13 languages. Voxtral Mini Transcribe V2 is a batch audio model at $0.003 per minute that focuses on accuracy, speaker diarization, context biasing for up to 100 phrases, word-level timestamps, and up to 3 hours of audio per request, targeting meetings, calls, and long recordings. Voxtral Realtime (Voxtral Mini 4B Realtime 2602) is a 4B parameter streaming ASR model with a causal encoder and sliding-window attention, offering configurable transcription delay from 80 ms to 2.4 s, priced at $0.006 per minute and also released as Apache 2.0 open weights with official vLLM Realtime support. Together they cover offline analytics, compliance logging, and low-latency voice agents on a single 16 GB GPU.....… Read the full analysis/article here.

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150+ more open codes/notebooks here ➡️