current models and inference stacks spend much of their time bottlenecked by a single hardware resource (e.g. memory bandwidth or compute throughput), while other resources remain underutilized.

techniques like speculative decoding improve utilization significantly, but we are still far from the ideal of how efficient model serving could be. manywarp conducts computer systems and model architecture research to bring us closer to that ideal.

the result is lower latency, higher throughput, and more cost-effective, energy-efficient inference.

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