Aditya Kamat — AK·OS

Aditya Kamat — LLM Inference Engineer & multidisciplinary creativeI work on the performance-engineering side of LLM inference: speculative decoding, KV-cache, quantization, and the systems around them, with open-source PRs to SGLang and vLLM. MS Data Science, Stony Brook University (2026). Open to full-time roles. There is also a creative side: music, DJ, video editing, 3D and VFX.Selected technical work

SGLang & vLLM, open-source contributions: suffix decoding for SGLang (2.6× throughput on Blazedit at H200), MXFP4/MXFP6 quantization refactor and GPU UUID support in vLLM, and an EAGLE KV-cache OOM fix. SGLang #13553, vLLM #32120.

The Corruption Radius: post-rejection logits in speculative decoding; d=3 reaches 90.9% acceptance agreement, and guided draft reuse gives +1.54% wall-clock throughput at zero extra target passes. Write-up on Medium.

The Sandbox: seven frontier LLMs and humans playing social deduction over one WebSocket protocol (FastAPI + async persistence), at Stony Brook. Source.

pi-customizations: an orchestration layer over a minimal TypeScript coding agent (plan mode, crash-isolated sub-agents, live session diff). Source.

NerveX: a tree-sitter codebase-intelligence tool for AI coding agents, 4–11× token reduction, on PyPI. PyPI.

RTGX: real-time lecture glossary explainer (Faster-Whisper to Groq Llama 3.3 70B), sub-500ms transcription latency. Source.

ScanAI: vision-LLM ingredient analysis, full-stack on AWS with 99.5% uptime. Source.

LLM-driven robot manipulation: Unreal simulator with neural SDFs and SPH for pouring and grasping, 45% fewer failures (Stony Brook Interacting Robotic Systems Lab).

Go-RAFT: Raft consensus from scratch in Go, deterministic recovery in under 500ms across failure modes. Source.

Service: artifact evaluation committee, IEEE HPCA 2026.CreativeMusic and DJ (DREAMS, debut single, on Spotify), video editing, 3D and VFX, motion and animation. Sets and mixes on Instagram.Contact & links

Email: adityakamat007@gmail.com

GitHub · LinkedIn

Résumé: ML / Inference (PDF) · Software Engineering (PDF)