Narwhal is adaptive hot-swap disaggregation for LLM inference on a role-free fleet, so GPUs move between prefill and decode instead of being pinned to one role. It ships with documentation for deploying, measuring and operating the fleet.
Be the first to rate Narwhal. Your feedback helps other users decide.
Docs cover discovering the deployment, installing an approved revision, validating and starting engines, qualifying the transfer fabric and attesting live engines.
Measuring covers the measurement contract and profiling, targets and a deployment freeze, a synthetic load trial and reconciling accepted benchmark points.
Control covers engine restart and process replacement, upgrade and rollback with release drills, and dashboards read through Grafana.
Similar tools in the same category — compare side-by-side
Senro is a WebMCP evaluation and observability platform. It tests your WebMCP tools and monitors how...
Wy turns an uncommitted diff into the questions you would need to answer to accept it, and shows whi...
Seahaven is an open-source Python framework for building synthetic worlds: working copies of your ag...
Guides and comparisons about code tools
Eleven Show HN launches for the unglamorous middle: vector search, Postgres indexes, waste audits, budget breakers, routing, inference fleets and optimizers.
A 2026 guide to AI code tools for data science: GitHub Copilot, Codeium, CodeWhisperer, Cursor, Codium AI, Replit, Noteable, Hugging Face, LangChain, Phind,
Three shifts have changed what AI does for developers, from autocomplete to repo-wide edits to review agents. What each category is actually good for.
An honest 2026 look at AI bug-fixing tools: Codium AI (Qodo), BugHunter AI, DeepCode, GitHub Copilot, and why a human must verify the fix.
A 2026 comparison of AI API-testing tools: AutoTest AI, TestGuard AI, APIBuilder AI, Bito. Covers E2E tests, auto-docs, and why human review still matters.
