DeepSeek-V4 Tackles Million-Token Context on NVIDIA HGX B200

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

Together AI has released OSCAR (Offline Spectral Covariance-Aware Rotation), an INT2 KV cache quantization method for long-context LLM serving. Unlike prior rotation-based approaches that apply data-oblivious Hadamard transforms, OSCAR derives separate rotations for keys and values from attention-aware covariance structures estimated offline. At 2.28 bits per KV element, OSCAR reduces the BF16 accuracy gap to 3.78 points on Qwen3-4B-Thinking-2507 and 1.42 points on Qwen3-8B, while delivering approximately 8× KV memory reduction and up to 3× decode speedup at 100K context length. The post Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving appeared first on MarkTechPost.

DeepSeek-V4 models are open and low-cost models that also use Chinese chipmaker Huawei's AI chips for inference.

DeepSeek-AI has released a preview version of the DeepSeek-V4 series: two Mixture-of-Experts (MoE) language models built around one core challenge making one-million-token context windows practical and affordable at inference time. The series consists of DeepSeek-V4-Pro, with 1.6T total parameters and 49B activated per token, and DeepSeek-V4-Flash, with 284B total parameters and 13B activated per token. […] The post DeepSeek AI Releases DeepSeek-V4: Compressed Sparse Attention and Heavily Compressed Attention Enable One-Million-Token Contexts appeared first on MarkTechPost.

The latest set of open-source models from DeepSeek are here. While the industry anticipated the dominance of “closed” iterations like GPT-5.5, the arrival of DeepSeek-V4 has ticked the dominance in the favour of open-source AI. By combining a 1.6 trillion parameter MoE architecture with a massive 1 million token context window, DeepSeek-V4 has effectively commoditized […] The post DeepSeek-V4: The Most Powerful Open-Source Model Ever appeared first on Analytics Vidhya.