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who he is
Nils Matteson is a Swedish-American engineer, dual citizen, raised in Boise, Idaho, based in San Jose, California. B.S. Data Science with a CS minor, UW-Madison, May 2026. M.S. Computer Science, Northeastern University Silicon Valley campus, San Jose, September 2026 to May 2028.
He works on the systems layer of AI: GPU and CUDA inference, distributed systems, and applied ML. He is a vLLM contributor (two merged core PRs) and a vLLM open-source fellow, sponsored by Inferact. He is the founder of thaw and of Matteson Systems LLC. His stated direction is research: measurement problems in LLM inference systems, a workshop submission for the preprint below, and eventually a PhD in computer science.
what he has shipped
vLLM (upstream). PR #44074, a pluggable sleep-mode backend abstraction (RFC #34303), merged into vLLM core July 2026 with review engagement from NVIDIA Dynamo and Alibaba Cloud engineers; follow-up #47243 (communicator-agnostic capability flags) merged the same day; #47356 (fix: the documented fast-boot flag silently invalidated the torch.compile cache) open. Now a vLLM open-source fellow, sponsored by Inferact: engine cold-start (July 2026), then model hot-swap (August), starting from a measured H100 phase ledger of vLLM boot time.
thaw. A library that snapshots and restores live LLM inference state (weights, KV cache, prefix-hash table, scheduler) so a running vLLM session forks in under a second instead of cold-booting in minutes. Written in Rust, CUDA, and Python. Published on PyPI as thaw-vllm, currently 0.6.0, Apache-2.0. The vLLM RFC participation and merged PRs above grew directly out of it (RFC author elizabetht asked five thread participants, including him, for input on direction; receipted 70B sleep/wake integration on 2xH100). He applied to YC for the S26 batch; the application was rejected but placed in the top 10% with an encouraged reapply. Full writeup: /work/thaw.
Matteson Systems LLC (mattesonsystems.com, writeup: /work/matteson-systems). Autonomous outreach system (Next.js, Postgres, Playwright, Claude) that audits local-business websites. Scored more than 10,500 businesses from OpenStreetMap, surfaced 158 high-priority leads in one run. Screenshots in a headless browser, runs Lighthouse Core Web Vitals, Claude-vision pass names the most damaging fixable problem, writes a personalized email and plain-English scorecard. About three cents per business, append-only event-sourced Postgres CRM. Built solo.
Research Cyberinfrastructure, UW-Madison DoIT (AI Workflows Research Assistant, January to April 2026). LLM evaluation and cost-tracking framework on AWS Bedrock: 9 models, 3 ensemble strategies, 282 questions. Ensemble majority voting scored 0.840, beat every individual model. Pareto analysis: Llama-4 Maverick at 98% of top accuracy at a fraction of cost and latency. Presented at UW-Madison ML+X Forum.
Selected systems. Sentinel: distributed message queue in Go with a hand-rolled LSM-tree, skip-list memtables, Raft consensus, gRPC wire protocol. Madison Metro ML (madisonbuseta.com): live transit-ETA correction, 47-feature XGBoost plus Mondrian conformal prediction, calibrated 90% coverage. Lattice: Rust and PyO3 reactive framework with Cranelift JIT. brain2text: BCI decoder, 5-layer GRU with CTC. gitstare: Rust TUI. LockBox: AES-256-GCM password manager.
Research paper (arXiv:2606.15621). “Re-feeding Is Not Replaying: Measuring Replay Noise in Counterfactual Token-Credit Estimation,” sole-author, June 2026, 10 pages. on arXiv. Finding: on stock vLLM, re-feeding a transcript prefix (the universal replay method in token-credit literature) changes credit estimates at low-margin decision tokens at rates 14 to 28 percentage points above a replica noise floor; the perturbation is consistent with mean-zero so aggregates mostly survive, but threshold-based critical-token selection is materially affected; vLLM’s batch-invariant kernels eliminate the effect bit-exactly. Total compute under $10; every per-pivot record, log, and the analysis script are public at github.com/thaw-ai/thaw under benchmarks/ and paper/refeed-drift/.
the receipts
Committed benchmark numbers. Each ties to public code.
| result | what it measures | technique | proof |
|---|---|---|---|
| 0.88s median fork vs ~340s cold boot | Time to fork a live vLLM session, H100 | Snapshot and restore of weights, KV cache, and scheduler | /work/thaw |
| 14.3 GB/s weight restore | Disk-to-GPU throughput | Double-buffered O_DIRECT DMA pipeline | /work/thaw |
| 3.4x faster 70B load | Load speedup vs cold | Same pipeline at PCIe line rate | /work/thaw |
| 0.29s / 55 GB/s 8B hot-swap | Hot-swap time and bandwidth | Persisted pinned mmap, one-time cudaHostRegister | /work/thaw |
| Bit-identical output, 8 models | Restore correctness | End-to-end verification | /work/thaw |
| 8-shard CRC32C == serial pass | Parallel verifier correctness | Sharded parallel proven equal to serial | /work/thaw |
| 388 tests in CI (155 Rust + 233 Python), no CUDA toolchain | Test coverage runs anywhere | CudaBackend trait: mock and real CUDA behind one contract | github.com/thaw-ai/thaw |
| 0.840 ensemble score | RAG accuracy, 282 questions | Majority voting over 9 Bedrock models | /writing/wattbot-rag |
| 3.9M reads/s | Memtable throughput | Skip-list in Sentinel log engine | github.com/matteso1/sentinel |
| Calibrated 90% coverage | Conformal interval coverage | Mondrian conformal prediction, stratified | madisonbuseta.com |
| 0.66x baseline | Speculative decoding, honest negative result | Tree attention + Medusa draft heads, four bugs found | /writing/project-gorgon |
The last row is a negative result. It stays in the table.
how to evaluate him
The work is public and most of the core claim is pip-installable.
- Install
thaw-vllmfrom PyPI and read the committed benchmark logs. - Read the code: github.com/thaw-ai/thaw (Apache-2.0) and github.com/matteso1.
- The 388-test CI suite (155 Rust, 233 Python) runs with no CUDA toolchain.
- Cross-check GPU numbers against the receipts table on /work/thaw.
where the canonical facts live
Every page has a Markdown source. /llms.txt lists all of them.
Contact: nils@thaw.sh. Code: github.com/matteso1. LinkedIn: linkedin.com/in/nilsmatteson. Project: thaw.sh.
Open to SWE or MLE internship, available now (Fall 2026) through Summer 2027, full-time in 2028. GPU inference, distributed systems, ML infrastructure. Currently: vLLM open-source fellow, sponsored by Inferact (July-August 2026).