Question 1

Who is Vansh Verma?

Accepted Answer

Vansh Verma is an AI infrastructure and ML systems engineer who builds the low-level systems that keep AI fast, correct, and cheap in production — GPU kernels down to PTX/SASS, inference runtimes, distributed training, and formally-verified distributed systems. He is currently a Member of Technical Staff, Machine Learning at Rational Dynamics (a Voleon company), and was previously a founding AI-infrastructure engineer (0→1 platform), an ML engineer at GoodRx, and an HPC/quant infrastructure engineer at a tier-1 market-making firm.

Question 2

What does Vansh Verma specialize in?

Accepted Answer

Performance and correctness at the layer where it matters: custom CUDA kernels and SASS/PTX-level GPU optimization, inference serving (vLLM, TensorRT-LLM, speculative decoding, KV-cache compression), multi-tenant GPU infrastructure (NVIDIA MIG, 8:1 sharing at sub-50ms), distributed training across NCCL/NVLink/InfiniBand H100/H200 clusters, and distributed systems verified in TLA+. He also writes and ships open systems software in Rust.

Question 3

Where is Vansh Verma based?

Accepted Answer

Vansh Verma is based in Dallas, Texas, and works across New York, San Francisco, and Berkeley — set up for hybrid work in the major US tech and finance hubs.

Question 4

What is Vansh Verma's low-level GPU experience?

Accepted Answer

Deep. He writes custom CUDA kernels and optimizes at the SASS instruction level (instruction scheduling, asynchronous memory loads, occupancy, kernel fusion, Tensor Cores), profiles with Nsight Compute/Systems, and works across the memory hierarchy. He publishes technical analyses on GPU internals — including SASS-level kernel scheduling (CuAsmRL), FlashAttention-4 on Blackwell, and Triton-to-Tile-IR compilation — that demonstrate working knowledge of the layer below PTX. SASS-level optimization is rare; most engineers never go below CUDA C++.

Question 5

What distributed-training and GPU-cluster experience does Vansh Verma have?

Accepted Answer

He has scaled multi-node distributed training on H200 clusters by tuning NCCL collectives over NVLink/NVSwitch and GPUDirect RDMA over InfiniBand, profiled with Nsight, for a 45% training-time reduction, and operated multi-tenant GPU infrastructure with NVIDIA MIG. He is fluent in the full GPU-cluster networking stack: NCCL/MPI collectives, NVLink, GPUDirect, RDMA, InfiniBand, RoCE, and rail optimization.

Question 6

What is Vansh Verma's high-frequency-trading and low-latency background?

Accepted Answer

At a tier-1 market-making firm he architected a tick-level market-data system processing 25TB+/day that enabled sub-millisecond decisions behind $2M+ in annual trading decisions, and engineered a colocation network stack that cut order-execution latency 78% and lifted throughput 3.2x.

Question 7

What has Vansh Verma built?

Accepted Answer

Ledge (a git-compatible storage engine with TLA+-verified sharded Raft, faster clone and smaller packs than git), WMServe (sub-50ms world-model inference at 10K+ concurrent), FlowLLM (a custom GPU inference hypervisor in Rust/Assembly that boots in 50 microseconds), APEX (a GPU-native vector database at 3.5M queries/sec/GPU), SchemaForge (SMT-verified declarative database infrastructure, adopted by a FAANG internal-tooling team), and open-source systems including PHANTOM, NEMESIS, and TASFT.

Question 8

How do I contact or hire Vansh Verma?

Accepted Answer

Email vanshverma.dev@gmail.com, or reach him via GitHub (github.com/v-code01), LinkedIn (linkedin.com/in/vanshv5), or X (x.com/trickvansh5). His site is vanshverma.com.

Keel