Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving

The post Together AI Claims Fastest Speech-to-Text Stack with Parakeet v3 appeared on BitcoinEthereumNews.com. Felix Pinkston May 29, 2026 22:48 Together AI unveils its fastest ASR stack, leveraging NVIDIA Parakeet v3 and Whisper for real-time, low-latency transcription. Details on the tech and market impact. Together AI has announced what it claims to be the fastest speech-to-text (ASR) stack in the world, capable of transcribing 20 hours of speech in under 10 seconds. The breakthrough leverages NVIDIA’s Parakeet-TDT 0.6B v3 and OpenAI’s Whisper Large v3, both optimized for low-latency and high-throughput applications. This development could significantly advance real-time voice AI systems, a key area of focus for the company as it scales its infrastructure. The heart of Together AI’s achievement lies in treating ASR as a full-path systems problem, rather than focusing solely on GPU inference. This holistic approach addresses bottlenecks across preprocessing, GPU exe

NVIDIA researchers have released Nemotron-Labs-Diffusion, a language model family that unifies three decoding modes in one architecture. The model supports autoregressive (AR) decoding, diffusion-based parallel decoding, and self-speculation decoding. It is available in 3B, 8B, and 14B parameter sizes. The family includes base, instruct, and vision-language variants. Sequential Decoding Limits Throughput Standard autoregressive (AR) language […] The post NVIDIA AI Releases Nemotron-Labs-Diffusion: A Tri-Mode Language Model with 6× Tokens Per Forward Over Qwen3-8B appeared first on MarkTechPost.

The post DeepSeek-V4 Tackles Million-Token Context on NVIDIA HGX B200 appeared on BitcoinEthereumNews.com. Luisa Crawford May 11, 2026 18:55 DeepSeek-V4 introduces a 1M-token context window with a hybrid attention architecture, shifting the challenge to inference systems on NVIDIA hardware. DeepSeek-V4, launched by Together AI, is reshaping how AI handles ultra-long context windows by introducing a 1-million-token capacity. Rather than simply a model architecture breakthrough, V4 transforms this into a systems-level challenge, focusing on efficient inference and memory management. This innovation runs on NVIDIA HGX B200 hardware, leveraging advanced techniques like compressed Key-Value (KV) layouts, prefix caching, and hybrid attention mechanisms to address the bottlenecks of long-sequence processing. Architectural Shifts: Compressing the Token Axis At the core of DeepSeek-V4’s advancements is a hybrid attention mechanism that compresses the token axis before KV stora