Who it is forA user or developer running long-document Q&A or a long multi-turn session on a local Strix Halo server.
The problemEach follow-up turn on a long context normally costs a full prefill, which dominates wait time at 32K tokens.
The outcomeFollow-up turns on a long conversation start quickly, with answers that match a cold run.
What you need first
- AMD Strix Halo machine (Radeon 8060S class GPU, gfx1151) with /dev/kfd and /dev/dri available
- Podman (or Docker with the video and render group flags described in the README)
- About 36 GB of free disk for the 35.9 GB checkpoint, plus the 3.5 GB image
- Python with pip to install the Hugging Face CLI
Steps
- Start the server with the default settings from the run command.
- Send the long document in the first chat request to /v1/chat/completions on port 8731.
- Send a follow-up request in the same conversation that reuses the same earlier messages.
- Compare time to first token for the first and follow-up requests.
- Set HALOGEN_CACHE_MB to 0 only if you want to disable the prompt cache for a comparison run.
Prompt for your agent
In the repo peonist-ai/halogen-server, run the halogen:0.1.4 server, send a long document from <document_path> as the first chat turn, then send the follow-up question <question> in the same conversation. Expected output: a faster time to first token on the follow-up than on the first turn. Verify that the follow-up answer is the same as a cold run with the cache disabled.
Caveat. The README gives roughly 20 times faster time to first token at 32K as a measured figure, so expect different gains on other prompt sizes.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for peonist-ai/halogen-server and "Continue long chats without re-processing the context"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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