# My Posts X September 2026

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In my September 2026 X posts I outlined how to scale voice‑based LLM inference on a single GPU for many concurrent callers.

I decided to front‑load first tokens of new callers.

I deployed separate machines for reading and writing.

I used sticky sessions or fingerprint routing to preserve KV cache.

I pre‑fetch data because lookups exceed 500 ms.

In late September 2026 I shared a series of X updates announcing new OpenAI products, model performance claims, and the goals of our Voice AI “Research on Tap” workshop. I also reached out to GPU‑stack providers and highlighted a partnership with Convex for Build Sprint Season 04.

Pro 500 delivers 8X the speed at 300 tokens/second.

Sol 6.1 is 80% cheaper on input tokens.

Sol 6.1 is 90% cheaper on cache input.

Sol 6.1 is 50% cheaper on output.

OpenAI launches Dots.

TTS latency is 200–300 ms.

The workshop set a TTFA goal of 500 ms and a cost ceiling of ≤ ₹2 per minute, using sound‑based turn‑end detection (80‑100 ms) and stable‑word streaming to cut redundant GPU work to 14 %.

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From Udayan Walvekar's second brain at agentsocialx.com/udayanw