abhinandan

Abhinandan

I do RL post-training on reasoning models, & build the inference systems that serve them.

i like working when the world is sleeping. my work cycle is generally 12pm to 4am. And I usually write my thoughts in my artifacts .

I live the part after the launch day. When the reasoning breaks, when the cost skyrockets, when the first traffic hits - all the similar thrills :)

Artifacts

  1. 16

    one of the most useful optimizations in LLM inference is KV caching. Instead of recomputing the same prefix every time, we store its previously calculated keys and values and…

    https://abhinandan.one/artifacts/building-a-quantized-l2-kv-cache-for-sglang-hiqcache · serial 16 · published 2026-10-12T05:59:11.509+00:00 · updated 2026-10-12T06:00:39.559+00:00
  2. 07

    I think if you have ever spent even a single day learning about current models, then attention is most probably the first term you may have heard. And when it comes to the…

    https://abhinandan.one/artifacts/understanding-attention-from-scratch · serial 07 · published 2026-10-06T17:30:00.202+00:00 · updated 2026-10-06T17:42:39.065+00:00
  3. 06

    These two terms are very common. And it's very possible that you may have heard them before, or maybe you even know what they mean. But I feel that even though earlier I knew…

    https://abhinandan.one/artifacts/prefill-decode-made-crystal-clear · serial 06 · published 2026-10-02T13:10:55.521+00:00 · updated 2026-10-02T13:58:38.825+00:00

Open source

Projects

RolloutCoreRL Rollout Runtime

Versioned RL rollout runtime for vLLM with NCCL hot weight updates, version-pure rollouts, and cache-coherent transitions.

stack: PyTorch, NCCL, vLLM, RL
keywords: LLM inference, serving runtime, continuous batching, chunked prefill, paged attention, KV cache, prefix caching, preemption, request scheduling, vLLM, TTFT, throughput
date: 2026-09-23 · case study: none
HiQCacheQuantized SGLang HiCache

INT8 hierarchical KV caching for SGLang HiCache, delivering 43.75% lower host KV memory and 1.78× more L2 cache capacity for Qwen3-8B.

stack: PyTorch, SGLang, Quantization
keywords: LLM inference, serving runtime, continuous batching, chunked prefill, paged attention, KV cache, prefix caching, preemption, request scheduling, vLLM, TTFT, throughput
date: 2026-09-23 · case study: none
MiniServeLLM Inference Runtime

a from-scratch LLM serving runtime with continuous batching, chunked prefill, block-based KV management, preemption, and prefix caching.

stack: Python 3.12, PyTorch, Paged KV Cache, Continuous Batching, Prefix Caching
keywords: LLM inference, serving runtime, continuous batching, chunked prefill, paged attention, KV cache, prefix caching, preemption, request scheduling, vLLM, TTFT, throughput
date: 2026-09-23 · case study: none
AgentFlow-ProAgentic RL Research

Process-supervised RL that made an 8B model reason better, and the gain carried over to a domain it never trained on.

stack: PyTorch, TRL, DAPO, PRM, PEFT / LoRA, Qwen3-8B, Ollama, FastMCP
keywords: AgentFlow, process reward model, DAPO, reinforcement learning, agentic reasoning, Qwen3, LoRA fine-tuning, GPQA, AIME, RLHF
date: 2026-05-20 · case study: https://abhinandan.one/agentflow-pro
SmartMemoSemantic LLM Cache

A semantic cache for LLM agents where a trained classifier decides if a cached answer is safe to reuse, instead of raw cosine similarity.

stack: FAISS, SentenceTransformers, PyTorch, SQLite, Pydantic
keywords: semantic cache, LLM cache, FAISS, embeddings, sentence-transformers, pairwise classifier, semantic equivalence, prompt caching, MiniLM
date: 2026-03-15 · case study: https://abhinandan.one/smartmemo
OrchflowAgent Orchestration Framework

A dependency-free Python framework for readable multi-agent pipelines: sequential, parallel, conditional, and resumable flows.

stack: AsyncIO, LiteLLM, Pydantic
keywords: multi-agent, orchestration, asyncio, pipeline, LiteLLM, workflow framework, checkpoint resume, dependency-free
date: 2026-02-20 · case study: https://abhinandan.one/orchflow
agentevalLLM Evaluation Tooling

Behavioral eval for agents: replaces brittle exact-match asserts with repeated-run pass-rate scoring for CI gates.

stack: AsyncIO, OpenAI SDK, Anthropic SDK, LangChain, Typer
keywords: agent evaluation, LLM testing, pass rate, CI, behavioral assertions, agent tracing, non-deterministic testing, regression tracking
date: 2026-01-25 · case study: https://abhinandan.one/agenteval

Work

BrowzerFounding Software EngineerSF
Sept 2025 to Oct 2026
Cynos NexusFounding Software EngineerNoida
2025
Etkin.aiContract Software EngineerTürkiye
2024
HeroUI (YC S24)Open Source ContributorRemote
2024

Amazon ML Summer School 2025 · IYMC Gold Honour · Top 1% TypeScript Engineer, Algora