๐ฅ๐๐ป๐ป๐ถ๐ป๐ด
@vllm_project๐ถ๐ ๐ฒ๐ฎ๐๐. ๐๐ฒ๐ฒ๐ฝ๐ถ๐ป๐ด ๐น๐ฎ๐๐ฒ๐ป๐ฐ๐ ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ฎ๐ฏ๐น๐ฒ ๐๐ต๐ฒ๐ป ๐ฏ๐ฌ๐ฌ ๐ฐ๐๐๐๐ผ๐บ๐ฒ๐ฟ๐ ๐ฎ๐ฟ๐ฟ๐ถ๐๐ฒ ๐ฎ๐ ๐ผ๐ป๐ฐ๐ฒ ๐ถ๐ ๐๐ต๐ฒ ๐ต๐ฎ๐ฟ๐ฑ ๐ฝ๐ฎ๐ฟ๐. Imagine a delayed flight. Hundreds of passengers call the airlineโs AI voice agent simultaneously. Requests queue, TTFT climbs, callers wait in silence. An engineer gets paged. Now your team is tuning configurations, rerunning load tests, and adding spare capacity for the next spike. Over-provisioning buys headroom but it also leaves you paying for that headroom between bursts. We built@rivvraito automate this operational work. You set the modelโs latency and throughput targets. Rivvrโs autopilot: โข Load-tests and tunes vLLM kernels, shipping ๐๐ฝ ๐๐ผ ๐ฎ๐ ๐ต๐ถ๐ด๐ต๐ฒ๐ฟ ๐ง๐ฃ๐ฆ โข Monitors metrics, adjusts cluster topology on the fly to ๐ฎ๐๐๐ผ๐บ๐ฎ๐๐ถ๐ฐ๐ฎ๐น๐น๐ ๐ธ๐ฒ๐ฒ๐ฝ ๐ฆ๐๐ข ๐๐ฎ๐ฟ๐ด๐ฒ๐๐ โข Optimizes compute costs, ๐ฐ๐๐๐๐ถ๐ป๐ด ๐ฐ๐ฌ-๐ณ๐ฌ% ๐ถ๐ป ๐๐ช๐ฆ ๐ฏ๐ถ๐น๐น โข Handles incident recovery, rollouts, and rollbacks For example, it can increase memory headroom to lower TTFT, migrate to lower-cost Spot instances, or switch VM sizes when AWS runs out of capacity. And these are just a fraction of Autopilot's capabilities. ๐ฌ๐ผ๐๐ฟ ๐๐ฒ๐ฎ๐บ ๐๐ฒ๐๐ ๐๐ต๐ฒ ๐ฟ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐๐. ๐ฅ๐ถ๐๐๐ฟ ๐ฟ๐๐ป๐ ๐๐ต๐ฒ ๐ถ๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐ฎ๐ฐ/๐ณ. Watch the demo below: 00:00