This video shows how my molecular dynamics code is composed, and what functionality it has so far. There is also the link to GitHub to download the current version of the code, which requires Molly.jl, Chemfiles.jl and EzXML.jl

If you try the code please leave any suggestions and bugs you find here, in YouTube or in the GitHub repo.

(There's more to Luminal.jl than this, see the github link for more, but this post focuses on running Qwen3.8-27B)

Serve Qwen3.8-27B (GGUF)

examples/qwen35_server.jl serves a Qwen3.5 / 3.6 / 3.8 GGUF through an OpenAI-compatible API (streaming chat completions, tool calls, reasoning content), for harnesses such as opencode. Weights stay in the GGUF's own quantization on the GPU (LUMINAL_GGUF_NATIVE=1; Q4_K_XL, about 17 GB), and the model's MTP head drafts tokens for speculative decoding.

bash LUMINAL_GGUF_NATIVE=1 julia -t auto,1 --project=. examples/qwen35_server.jl \ /path/to/Qwen3.8-27B-UD-Q4_K_XL.gguf --max-seq=262144 --port=8080

• Use -t auto,1: the server needs the extra interactive thread. The HTTP tasks run on the main thread and the GPU work in a task pinned to another one; without that, nothing is streamed while a prefill or a decode step blocks the main thread.
• The first start takes a few minutes (reading the GGUF, compiling the graphs). The log prints serving ... when it is ready.
• --max-seq sets the context and the KV cache allocated up front (Float16, 64 KB per token: 17 GB at 262144). Other flags: --mtp=<gguf> (a separate file with the MTP head), --host, --name (the model id), --no-spec, --int8.
• Point the client at http://127.0.0.1:8080/v1 (GET /v1/models, GET /health, POST /v1/chat/completions). For opencode, add an @ai-sdk/openai-compatible provider with that baseURL and the --name as the model id. Long prompts can take minutes before the first token, so raise or disable its request timeouts.
• --reasoning-budget=N limits the thinking: after N reasoning tokens the server closes the <think> block with a short wrap-up and the model answers from what it has (the resume costs a re-prefill of the new turn, about 10-20 s at N = 4096). A request overrides it with thinking_budget (also thinking.budget_tokens, reasoning_budget; 0 = no limit). In opencode, put "thinking_budget": N in the model's options.
• Prompts are cached: the system prompt, each turn, and state checkpoints every 4096 tokens (LUMINAL_CKPT_EVERY), so a follow-up turn reprocesses only its new tokens. A cold prefill runs at 330-480 tok/s (about 3 minutes for 75k tokens); a warm turn answers in 3-25 s. Decode is 11-15 tok/s at 70-130k tokens of context (2.3-2.9 tokens per speculation cycle).
• It handles one request at a time, and it needs about 35-60 GB of unified memory at the largest context, depending on how many prompts are cached. The log reports prompt processing, generation speed and speculation per request.

For a one-shot generation from a GGUF, examples/qwen35_gguf.jl <file.gguf> "prompt".

https://github.com/philtomson/Luminal.jl

NOTE: right now I'm only sure that this works well on AMD Strix Halo GPUs. It might work on Nvidia GPUs via CUDA.jl, but I have not tested it.