Local-first inference
A 261,888-token context on a single RTX 3090. Quantised weights, KV-cache tuning, and lazy expert streaming buy frontier-scale context on desk hardware.
Bench online NODE 01 · SELF-HOSTED
An independent engineering lab. Language models, semantic memory, and autonomous agents — compiled for one machine, measured on the bench, and kept entirely in-house.
Inference, memory, and autonomy are separate instruments on the same bench — each with its own resource budget and its own failure mode.
A 261,888-token context on a single RTX 3090. Quantised weights, KV-cache tuning, and lazy expert streaming buy frontier-scale context on desk hardware.
Four tiers of persistence: atomic fact extraction, scene synthesis, persona modelling, and recall — run on dedicated CPU models so the accelerator stays free.
Repo-scale indexing, verifiable execution loops, and skill distillation. Agents propose, run, and prove their own changes against real builds before anything ships.
Four stages from the web-facing agent to local inference. Select a component to inspect what enters, what happens, and what leaves.
The reply travels back through the same chain. Tool calls can start another round.
freenginx accepts the connection and routes it to the intended application. Hostname rules, authentication, and network allow-lists keep internal services behind their own access boundaries.
This page is a static origin. Visiting it does not send a request to the model.
The agent assembles the conversation, requests a completion, and executes any tool calls. Tool results return to the conversation for the next model round; the loop ends with a final answer.
Local CLI agents enter here directly, without the web ingress hop.
The memory proxy enriches the request with relevant context before forwarding it. On the return path, round-level capture stores the completed exchange rather than duplicating every intermediate tool round.
Extraction, synthesis, and embedding run on dedicated CPU services, separate from GPU inference.
llama.cpp evaluates the prompt and generates tokens on the RTX 3090, using host memory and expert streaming to fit the workload. The response returns through the proxy to the agent.
A tool-call response resumes the agent loop; a final answer completes the turn.
L1 extraction · L2/L3 synthesis · ONNX embeddings
systemd isolation · health gates · versioned rollback
Nominal specifications of the machine everything runs on. Figures are the build sheet, not a live utilisation dashboard.
| Ref | Component | Specification | Notes |
|---|---|---|---|
| U1 | NVIDIA RTX 3090 | 24 GiB GDDR6X · SM86 | KV cache, expert LRU and the CLIP projector are resident at once — the card runs deliberately near saturation. |
| U2 | Ryzen 5 5600X | 6 cores · 12 threads · Zen 3 | Six threads to inference, the remainder to memory extraction and the edge. |
| U3 | Host memory | 125 GiB DDR4 | Streams an 85 GB weight file without thrashing the page cache. |
| U4 | freenginx 1.31.4 | Source build · OpenSSL 3.5 · LTO | PCRE JIT and -march=native; one edge for every hostname. |
mail contact@dgonzo.de
Technical collaborations, infrastructure experiments, and long arguments about scheduling are all welcome. Replies come from a human on this machine.