NVIDIA GB300 NVL72 tokens per watt advantage over Hopper reaches 25x on DeepSeek V4 Pro per SemiAnalysis InferenceX data, ...

Deploying DFlash block diffusion on NVIDIA hardware accelerates autoregressive LLMs during latency-sensitive inference. Autoregressive large language models generate tokens sequentially. This ...

Google has released DiffusionGemma, an experimental language model that generates text using a diffusion-based method, producing blocks of 256 tokens at once rather than generating text word by word.

To understand why this matters, it helps to first understand how most language models generate text today. Standard large language models are autoregressive: they decode one token at a time in ...

The AI image generation market has had an uncontested leader for months. Google's Nano Banana family of models has set the standard for quality, speed, and commercial adoption, while competitors from ...

In the field of generative AI media, the industry is transitioning from purely probabilistic pixel synthesis toward models capable of structural reasoning. Luma Labs has just released Uni-1, a ...

For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We ...

Z.ai announced their image generation model, GLM-Image, on January 14, 2026 (local time). It is a hybrid model that combines an autoregressive model and a diffusion model. The autoregressive model is ...

Chinese outfit Zhipu AI claims it trained a new model entirely using Huawei hardware, and that it’s the first company to build an advanced model entirely on Chinese hardware. Zhipu, which styles ...

Chinese AI startup Zhipu AI announced on Wednesday that it has partnered with Huawei to open-source GLM-Image, a new-generation image generation model that represents a state-of-the-art (SOTA) ...

Chinese company Zhipu AI has trained image generation model entirely on Huawei processors, demonstrating that Chinese firms can build competitive AI systems without access to advanced Western chips.

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...