"Mid-competition, a participant named Nathan Maine built a 30-second GPU benchmark script for Runpod pods and shared it with other competitors. Nobody asked him to. That kind of tooling gets built when the infrastructure is part of your workflow, not just a resource you're renting."

— RunPod, OpenAI Parameter Golf: what 1,100 researchers built in six weeks

Most program managers manage. Most builders build. The rare thing is one person who has done both at scale, because that person can translate between the boardroom and the terminal without losing anything in either direction.

Every claim on this page links to something you can check. The ones that don't fit in a link, ask me about.

A complete, reproducible recipe for serving Qwen3-Coder-Next (80B total / 3B active sparse MoE) as an OpenAI-compatible API on a single DGX Spark, with working tool-calling and real multi-user concurrency.

The decisive details were scattered across NVIDIA forum threads, GitHub issues in three repos, model cards, and a community Docker project: which checkpoint actually performs (a native int4 AutoRound quant, not a GGUF), the one environment variable the int4 kernels need, why driver 590+ matters (CUDA graphs on GB10: 2.8x single-stream speed), and the Secure Boot DKMS trap that hangs installs. This repo puts the working configuration, the reasoning behind every setting, and an honest account of every dead end in one place.

Confirmed by vLLM's server-side accounting, not client timers. 131K context. The repo documents which claims from its own first draft were wrong and got fixed before publishing.

GPU-native knowledge intelligence platform built on 6 NVIDIA technologies. Your experts. Your GPU. Your data never leaves.

Ingest domain expertise at scale, build GPU-accelerated relationship graphs with RAPIDS cuGraph, and serve answers through any OpenAI-compatible tool. Built on NIM, TensorRT-LLM, Triton, NeMo Guardrails, cuGraph, and CUDA.

Active development / reference architecture: built and tested (1,006 passing tests), not yet a turnkey end-to-end deploy. See the repo Status section.

13 fine-tuned LLMs across 8 architectures (7B-72B) for cybersecurity compliance: CMMC 2.0, NIST 800-171, HIPAA. Served fully air-gapped, because the organizations that need compliance AI most are the ones that cannot send data to a cloud API.

Flagship: Gemma 4 31B (eval loss 0.4517). QLoRA/DoRA fine-tuning, GGUF export, Ollama deployment. Training data: 18,747 curated compliance examples across 11 regulatory frameworks, rebuilt from 67K raw examples with 73% noise removed.

Turns a vague request into an explicit work order, so local coding models execute instead of guessing.

Born from a real failure: a local 80B model was asked to "build out the gtm adapters package." It wrote an __init__.py importing three sibling modules that did not exist, saw an ImportError naming __init__.py, and rewrote __init__.py. Ten times. The model was not broken; it was fixing the file the error named instead of creating the files that were missing. The lesson generalizes: local models follow explicit instructions well and infer badly. Ambiguity is what makes them loop.

So Promptx splits the job. A cheap, fast cloud model (~$0.10 per million tokens) converts intent into a precise, numbered spec with real file paths, because it reads your actual file tree instead of inventing plausible ones. Your local model, excellent at execution and weak at inference, runs the spec. Fraction of a cent per request. Zero dependencies: Python 3.9+ standard library only, on purpose. --snap records a baseline before the agent works; --check verifies what it actually did.

Competition work is under my dentity007 handle (which displays as Nathan Maine).

Training the best language model in 16MB on 8xH100s. Implemented all 7 of OpenAI's explicitly requested research directions, with 8 complete training scripts (11,810 lines of novel research code) and 95+ GPU experiments across RTX 5090, H100, and H200 SXM pods. Novel techniques developed beyond the requests: Adaptive Density Training, Echo Training, Gradient Quilting.

Companion dashboard: parameter-golf-experiment-lab, a live interactive visualization of 793+ scored community submissions with TTT-legality filtering, my 95+ experiment log, technique matrix, and cost analysis.

Ten years of technical program management. The systems above get built at night; these ship during the day.

• NVIDIA DGX Spark (GB10, 128GB unified memory) running daily: Qwen3-Coder-Next at ~70 tok/s solo via vLLM, Gemma 4 26B A4B at 43 tok/s
• 486K+ knowledge chunks from 80+ AI/ML experts
• 10G backbone connecting DGX Spark, NAS (3.6TB models), and workstations
• For heavy training: 8xH100 SXM on RunPod, torchrun DDP, torch.compile, FA3