The inference server evaluates inputs once to concurrently branch out multiple questions, returning choice probabilities alongside baseline metrics on empty content and unselected token residuals. It also executes structured extractions, such as specific text, amounts, and numbers, directly during this primary evaluation pass. The setup requires Linux, Python 3.12, CUDA 13 driver support, and a 24 GB NVIDIA GPU featuring fp8 tensor cores or Ampere cards running int8 weights.
Key points
The software replaces traditional chat generation by deriving structured decisions direct…: The software replaces traditional chat generation by deriving structured decisions directly from raw model probabilities.
Deployments can be handled using standard Python packages or Docker across any open instr…: Deployments can be handled using standard Python packages or Docker across any open instruction or dedicated decision model.
Older NVIDIA Ampere cards remain usable with the platform through the implementation of i…: Older NVIDIA Ampere cards remain usable with the platform through the implementation of int8 weights.
Sources checked by our analysis pipeline: 1
Bulk classification and decision inference on one GPU.
spinf-serve reads a piece of content once and answers many questions about it in the same pass. Each answer is a probability over the options you give, not generated text. It runs on one 24 GB NVIDIA GPU and serves an OpenAI-compatible API, the same API as the hosted spinf service. Apache 2.0.
We built this engine for our own work: a daily market index read from millions of news articles, each article asked dozens of narrow, calibrated questions. A serving engine built for chat re-reads the content for every question and generates one token at a time, which made that job slow and expensive. spinf-serve lays every question out as a branch of one read of the content and packs many contents into one forward pass. Bulk classification is where it started and where it is strongest.
The same mechanism is what is now called a decision model: a model that returns a probability over a fixed set of answers (yes or no, a label, a level) instead of a paragraph.…
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