lemonade
Runs inference on local hardware (GPU/NPU) without cloud dependencies, reducing latency, cost, and data residency risk for AI workloads.
Lemonade helps users discover and run local AI apps by serving optimized LLMs right from their own GPUs and NPUs. Join our discord: https://discord.gg/5xXzkMu8Zk
- Run LLMs locally for document analysis without cloud uploads
- Deploy language models on customer devices for offline chat
- Reduce inference latency for real-time code completion
Runs inference on local hardware (GPU/NPU) without cloud dependencies, reducing latency, cost, and data residency risk for AI workloads.
Teams needing low-latency LLM inference on-premises or edge devices without external API calls.
https://github.com/lemonade-sdk/lemonade
By lemonade-sdk
How to Get It
claude plugins install lemonade-sdk/lemonade
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me run LLMs locally for document analysis without cloud uploads
Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News, Reddit
- Thoughts from a lemonade worker — Reddit · 2741 pts
- Beyoncé released 'Lemonade' 10 years ago today — Reddit · 1539 pts
- Lemonade by AMD: a fast and open source local LLM server using GPU and NPU — Hacker News · 572 pts
3 mentions across 2 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 5,396 GitHub stars; 125 contributors; last commit 0d ago; license Apache-2.0.
2026-05-10: Lemonade is a local inference runner targeting GPU and NPU hardware — useful for teams with data residency constraints, air-gapped environments, or edge deployments where calling out to cloud APIs isn't an option. The 3.6k stars and 73 contributors suggest real traction, but production adoption is thin (one confirmed mention), so treat it as promising-but-unproven for anything critical. Worth benchmarking against Ollama or llama.cpp if you're already in that space, as those have wider production track records and larger model support matrices.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 5,396 GitHub stars; 125 contributors; last commit 0d ago; license Apache-2.0.