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Paddock 0.1.12

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@panterlo panterlo released this 01 Oct 11:32
v0.1.12
6d725da

A tables release: Kumo Tabular, NVIDIA's model that predicts a missing
column from labelled rows, runs natively on NVIDIA GPUs and on the Mac, with
a Tables page in the Studio and the Mac app. Requests with many images or
long audio use far less memory, Gemma 4 no longer fails on the DGX Spark and
RTX 50-series cards with an f16 KV cache, and long Qwen 3.8 Flash-Next
prompts prefill several times faster. Windows x64, Linux x64 and the NVIDIA
DGX Spark. NVIDIA GPUs, driver 580 or newer. The macOS pre-release for Apple
Silicon is built from the same commit.

New

  • Kumo Tabular. Give it labelled rows and it predicts the missing column
    of new ones - a class with its confidence, or a number with an 80% range -
    with no training: the labelled rows are read in context on every call.
    Small, medium and large, each for classification and for regression, in
    the F32 weights NVIDIA ships, behind the same /v1/tabular/predictions
    API on NVIDIA GPUs and on the Mac.

  • Tables in the Studio and the Mac app. Paste or open a CSV, pick the
    column to predict, and the rows with it empty come back filled in; copy
    the result as CSV, or see the equivalent curl. Every table and its runs
    are saved and shared between the web Studio and the Mac app, and each
    table has its own link.

Improved

  • Pictures cost far less memory. The images in a prompt are encoded as
    each prefill pass needs them, into memory the plan sets aside, instead of
    all at once and held twice. On the DGX Spark an 18K-token prompt with a
    picture peaks 1.3 GB above idle instead of 13.6 GB. This covers the Qwen
    3.5-3.8 models, Gemma 4, Granite Vision and PaddleOCR-VL.

  • A Qwen prompt with pictures no longer pauses other conversations while
    its pictures are read: on the DGX Spark the longest pause another session
    saw went from 15.4 s to 3.3 s.

  • Long audio clips use a fixed amount of memory in Qwen3-ASR and Granite
    Speech, with the same transcript as before.

  • Qwen 3.8 Flash-Next prefills long prompts much faster on its NVFP4
    versions - a 16.5K-token document question on the DGX Spark went from
    85 s to under 15 s - keeps its prefix cache across agent sessions, and an
    exact re-send of a long prompt resumes from its last walk instead of from
    the start.

  • An idle server hands the memory it freed back instead of holding it
    until the next request.

  • Quieter logs. A warning now means a client was held up or a real
    fallback happened; long prompts and normal startup no longer warn.

Fixed

  • A Qwen 3.8 server on the DGX Spark could run out of memory after about a
    day:     startup set aside up to 20 GB it never used. Under memory pressure
    the system now stops the runner, not the manager.

  • Gemma 4 with an f16 KV cache failed every request on the DGX Spark and
    RTX 50-series cards.     It now serves there.

  • A long Gemma 4 prompt with a picture could get a wrong answer when the
    picture fell across two prefill passes.

  • Safetensors checkpoints start from the Studio. The NVFP4 and FP8
    versions of Qwen 3.8 Flash-Next, Nemotron and Granite were refused when
    started from the Studio.

  • Switching Vision off switches it off. A vision file beside the model
    is no longer loaded anyway, and saving the endpoint through the Advanced
    tab keeps Vision off.

  • The start form offers document and image features only to models that
    chat.

macOS (pre-release)

  • Kumo Tabular runs natively on Metal, with a native Tables workspace.
  • Qwen 3.8 Flash-Next decodes and prefills faster on Metal.

Known

  • Laya reads text only.

  • Laya's confidence on choices with more than ten options is not
    calibrated:     the English checkpoint ships an out-of-range temperature for
    that case, which Paddock clamps, as Laya's own server does.

  • Kumo Tabular predicts up to ten classes.

  • On Qwen 3.8 Flash-Next's NVFP4 versions a greedy speculative reply can
    part from plain decoding at a near tie     - the same model with slightly
    different numerics, not a wrong answer.

  • On-demand loading covers Whisper only.

  • The fp8 KV cache's paged attention on RTX 50-series cards shows a small
    numeric deviation in one split configuration (#5).

Paddock 0.1.12, built from 6d725da3 on the maintainers' machines (Windows natively, Linux x64 in a Docker builder, the DGX Spark natively on a Spark), each archive booted on a GPU and asked a question before it was uploaded.

Requirements

  • An NVIDIA card the kernel pack covers - x64: RTX 30/40/50 series, the RTX A / PRO line, B200; aarch64: the DGX Spark (GB10). Other cards get an honest refusal, never a slow fallback. This build: sm_86 sm_89 sm_100 sm_100a sm_120 sm_120a.
  • A driver from the r580 branch or newer. No CUDA toolkit: the kernels and the runtime are inside paddock-runner.
  • Windows 11 x64; Linux x64 with glibc 2.35 or newer (Ubuntu 22.04+, Debian 12+); or a DGX Spark on DGX OS 7 (Ubuntu 24.04, glibc 2.39).

Run

  • Unzip or untar anywhere. paddock starts the manager and the Studio on http://localhost:11500. paddock-runner --model <file.gguf> serves a bare OpenAI/Anthropic-compatible endpoint. README.txt inside says the rest.

sha256

38cdc1ab5cdbe939ac071ecf9372bbfbc446697b6e3a83c45136a7a2cb53cab6  paddock-0.1.12-x86_64-windows.zip
7542bf06fdbf7de4537ea7261bdd430191e5499495a93c8270818e4655e5c6d2  paddock-0.1.12-x86_64-linux.tar.gz
aae665bd78ff47ebf6486c9525ab7d35708bc85874b4febc2a41b8ff95e3d398  paddock-0.1.12-aarch64-linux.tar.gz
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