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How to compare speculative and target-only generation with Lithos Metal

Run the same model with DSpark drafting on and off to see how the setting changes output speed.

Who it is forA performance-minded developer who wants to check whether DSpark speculative decoding helps on their own Mac and workload.

The problemThe README gives projections, not end-to-end results for every machine, so the developer needs to measure the difference locally.

The outcomeTwo server runs, one with the default DSpark draft and one with target-only generation, with timings recorded for the same prompt.

What you need first

  • An Apple silicon Mac running macOS 26 or later
  • Enough unified memory for the target model, draft model, and context (the README tests Qwen models on a 40-core M5 Max with 48 GB)
  • Homebrew, or Xcode Command Line Tools and Python 3.12 for a source build
  • Client tools such as opencode, claude, codex, or hermes installed separately, if you use the launchers

Steps

  1. Install the package with brew install lithos-ai/tap/lithos-metal.
  2. Start the default server with lithos-metal serve --model nvidia/Qwen3.8-27B-NVFP4 and time a fixed prompt with the curl call from the README.
  3. Stop the server and restart it with lithos-metal serve --model nvidia/Qwen3.8-27B-NVFP4 --no-draft. (inferred, check the README)
  4. Repeat the same timed prompt and record the tokens per second for both runs.

Prompt for your agent

Using the lithos-metal repo at https://github.com/lithos-ai/lithos-metal, run the same fixed prompt against `lithos-metal serve --model nvidia/Qwen3.8-27B-NVFP4` and again with `--no-draft`. Report wall-clock time and generated token count for each run in a two-row table. Verify both runs used the same prompt and max_completion_tokens before comparing.

Caveat. The README warns that its own speed figures exclude drafting and rewind and are not end-to-end comparisons, so your timings only apply to your machine, model, and prompt.

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