2025-10-10
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 work alongside large language models during inference. They draft multiple tokens ahead, which the main model then verifies in parallel. This technique (called speculative decoding) has become essential for enterprises trying to reduce inference costs and latency. Instead of generating tokens one at a time, the system can accept multiple tokens at once, dramatically improving throughput.
Together AI today announced research and a new system called ATLAS (AdapTive-LeArning Speculator System) that aims to help enterprises overcome the chall [...]
2025-10-04
A cycle-accurate alternative to speculation — unifying scalar, vector and matrix computeFor more than half a century, computing has relied on the Von Neumann or Harvard model. Nearly every modern ch [...]
2025-10-21
OpenAI's long-rumored browser has a name, and you can try it out today — provided you're an Apple user. ChatGPT Atlas is available to download on macOS, with the company promising to bring [...]
2025-10-22
Presented by ArmA simpler software stack is the key to portable, scalable AI across cloud and edge. AI is now powering real-world applications, yet fragmented software stacks are holding it back. Deve [...]
2025-10-13
Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing a technique that allows large language models (LLMs) — like those underp [...]
2025-10-29
Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time.The challenge is infrast [...]
2025-10-02
IBM today announced the release of Granite 4.0, the newest generation of its homemade family of open source large language models (LLMs) designed to balance high performance with lower memory and cost [...]