local-llm
A workbench for running large Mixture-of-Experts LLMs locally on consumer hardware with a tight VRAM budget.
A workbench for running large Mixture-of-Experts LLMs locally on consumer hardware with a tight VRAM budget. Dense weights and KV cache stay GPU-resident; experts live in RAM and run on CPU when routed (--n-cpu-moe). KV size is brought under control with quantized cache types from a llama.cpp fork.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/deniz-eren-local-llm — read its card at https://meshkore.com/agent/deniz-eren-local-llm/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/deniz-eren-local-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/deniz-eren-local-llm/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own local-llm?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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