Ollama: fix context deadline exceeded
Ollama context deadline exceeded means a timeout fired before the model answered. Find which layer set it: your client, the load, keep_alive, or nginx.
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Ollama context deadline exceeded means a timeout fired before the model answered. Find which layer set it: your client, the load, keep_alive, or nginx.
Size an LLM for a CPU server without guessing: weights in bytes, KV cache growth with context, runtime overhead, and why fitting is not the same as usable.
Two harnesses on one model can bill an order of magnitude apart. The mechanics behind the gap, and how to measure your own agent's token spend.
Your SearXNG instance can feed web search to Open WebUI. Enable the JSON format, then hand the other container a URL it can actually reach.
Ollama Cloud and a self-hosted Ollama share one CLI and one API. See exactly which settings change, and what leaves your machine on each path.
Pick a model by the RAM you actually have. Sizing arithmetic for 4 GB, 16 GB and 64 GB VPS plans, honest CPU token rates, and the hidden cost of context.
The KV cache is RAM your server pays for on every request. Prompt caching is a bill someone else discounts. Only one can break a model load.
Ollama truncates long prompts at a small default context window. Set num_ctx per request or server wide, and size the KV cache RAM before you raise it.
Ollama unloads your model after 5 minutes idle, so the next request pays the full load time again. Set keep_alive so it sticks, even after a reboot.
An LLM is weights that need RAM. An assistant adds a chat surface. An agent adds tools and a loop, and it holds credentials, so it needs a box that stays on.
Run Langfuse on your own VPS: the real resource floor, pinned image tags, TLS, ClickHouse retention before it fills the disk, and backups that work.
Ollama is a convenience layer for one user, on CPU if needed. vLLM is a throughput engine for GPUs. Pick by workload, with the real commands for both.
Claude's weights are not public, so no server of yours can run it. Here is what you can self-host instead: open models, a gateway, and Claude Code.
A 7B model wants 8 GB of RAM and gives 4 to 10 tokens per second on CPU. Host one on a VPS and call it at 127.0.0.1:11434/v1, with port 11434 kept closed.
OpenClaw is a personal AI agent that runs commands on your own server. Here is how one is built, piece by piece, and why hardening comes first.
An AI agent is a loop around a language model that can use tools. Learn the concepts behind building one on your own VPS: the loop, tools, MCP, memory.
Use Claude as the brain and your VPS as the body. How the Messages API, tool use, and MCP fit together to build an AI agent you run yourself.