Performance
Latency: The New Gold
David Kim
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Sep 15, 2025
In a world of abundant compute, time is the scarcest resource. Our recent H100 cluster optimization has yielded a 40% reduction in Time-To-First-Token (TTFT), fundamentally changing the feel of AI interactions from "processing" to "conversing".
The Physics of Inference
Latency comes from three distinct places: Network, Queue, and Compute. We solved Network latency with our Global Mesh, routing traffic to the nearest edge. We solved Queue latency with priority tiering for enterprise workloads. But Compute is where the real engineering happens. Standard PyTorch kernels often leave GPU cycles on the table.
Optimizing the H100 Kernel
We re-wrote our attention mechanism in Triton to bypass standard overheads. By fusing memory operations, we reduced the VRAM bandwidth bottleneck, which is the primary constraint for large batch inference. We also implemented aggressive KV cache quantization using FP8 storage for history, doubling our effective batch size without degrading model quality.
Furthermore, we moved to continuous batching. Instead of waiting for a full batch of requests to finish, we dynamically inject new requests into the GPU schedule as soon as capacity frees up, ensuring maximum utilization of the hardware.
Conclusion
50ms isn't just a number; it's the threshold of "instant" for human perception. Below this line, the AI stops feeling like an external service and starts feeling like an extension of the user's own thought process.
