2025-10-10
Together AI's ATLAS adaptive speculator delivers 400% inference speedup by learning from workloads in real-time
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 [...]
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Simplifying the AI stack: The key to scalable, portable intelligence from cloud to edge
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