Mani Pal

Engineer-researcher

Mani Pal

LLM systems, CUDA kernels, inference optimization, compression, interpretability, and distributed AI infrastructure.

EmailDelhi, IndiaGitHubLinkedIn

Open Source

Contributions, repositories, and research code

Highlighted contribution to vLLM — distributed inference engineering: request identity, KV cache transfer, and production reliability across prefill and decode nodes. Public repositories include LLM systems, CUDA kernels, and AI research implementations.

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@groot-code24

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GitHub repositories

Public projects and research code

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vLLMPR #388162025

Disaggregated prefill pipeline KV cache request-ID bug fix

Issue

Disaggregated prefill pipeline hang caused decode nodes to miss KV cache tensors.

Root Cause

Prefill and decode nodes used inconsistent request-ID formatting, so decode-side lookup could not locate the transferred KV cache state.

Patch

Implemented request-ID normalization at the prefill-decode boundary, refactored KV cache lookup semantics, and added targeted tests for matched and mismatched ID formats.

Technical Impact

Resolved indefinite hangs in distributed inference deployments and improved reliability for high-throughput disaggregated serving.

Engineering Complexity

High. The bug crossed request lifecycle, distributed KV cache transfer, and prefill/decode process boundaries.

Merged PR

PR #38816

vLLMDistributed InferenceKV CacheReliability