A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to digest long docs, tickets, and logs, this is a bid to get “long memory” without paying attention costs that grow with context length.The approach, called “End-to-End Test-Time Training” (TTT-E2E), reframes language modeling as a continual learning problem: Instead of memorizing facts during pre-training, models learn how to adapt in real time as they process new information.The result is a Transformer that can match long-context accuracy of full attention models while running at near-RNN efficiency — a potential breakthrough for enterprise workloads where context length is colliding [...]
Baseten, the AI infrastructure company recently valued at $2.15 billion, is making its most significant product pivot yet: a full-scale push into model training that could reshape how enterprises wean [...]
Cerebras Systems, the Silicon Valley chipmaker that built the world's largest commercial AI processor, erupted onto the Nasdaq on Wednesday, opening at $350 per share — nearly double its $185 I [...]
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]
For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity [...]
Enterprises expanding AI deployments are hitting an invisible performance wall. The culprit? Static speculators that can't keep up with shifting workloads.Speculators are smaller AI models that w [...]
Lowering the cost of inference is typically a combination of hardware and software. A new analysis released Thursday by Nvidia details how four leading inference providers are reporting 4x to 10x redu [...]
Researchers from Stanford, Nvidia, and Together AI have developed a new technique that can discover new solutions to very complex problems. For example, they managed to optimize a critical GPU kernel [...]
When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill.Researchers at MIT, the Improbab [...]
Even as the geopolitical conversation around AI continues to grow more fraught following the U.S. government's actions to limit the new models from Anthropic and OpenAI, Chinese open source darli [...]