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@github-actions github-actions released this 21 Oct 08:20
· 5 commits to main since this release

Engine

  • Data-parallel profiles build and restore on vLLM's DP frontend, which runs a
    /collective_rpc call on every engine replica but answers with the first
    replica's workers only. With dp > 1 warmctl calls its worker methods
    through the adapter's warmctl_rpc, which publishes each rank's result as a
    file in the agent's IO directory, and requires one file for each rank
    (#23). TP-only profiles and their snapshots keep the plain RPC.
  • Engines run with NCCL_GRAPH_REGISTER=0: NCCL's registration of the user
    buffers of graph-captured collectives goes through cuInterpose's memory
    adapter, and on the r615 user-mode driver the first replay of a larger
    decode graph hung every rank (#27). Without the registration the DEP8
    engine runs, checkpoints and restores; the registration only matters for
    large buffers.

Profiles

  • deepseek-v41-flash-1017-b200-dep8: the DEP8 recipe on 8×B200 (100 GiB of
    FP8 KV per GPU; 46 GiB of weights per rank). In the catalog for driver
    615: 809 GiB, restores in 84 s from the page cache, 38K output tokens/s at
    1K/1K once restored.
  • deepseek-v41-flash-1017-b300-tp8: DeepSeek-V4.1-Flash-1017 on 8×B300, one
    TP8 engine with expert parallelism, 256 sequences, 16K prefill chunks, 128 GiB
    of FP8 KV per GPU and DSpark with 5 draft tokens.
  • deepseek-v41-flash-1017-b300-dep8: the same model for throughput, after
    vLLM's B300 recipe: DEP8 (eight data-parallel attention replicas, EP with
    weight filtering), FlashMLA mega-attention with an MXFP4 sparse indexer,
    DeepGEMM mega-MoE, DSpark with block rejection sampling. Its image is the
    recipe's (vLLM 0.31) with libcuda 615 in place of 580
    (registry.warmctl.io/warmctl/vllm-openai:0.31-dsv41-b300-cu13compat615,
    deploy/images/vllm-cu13compat615): an r580 libcuda cannot restore the
    mega-MoE multicast buffers after cuda-checkpoint.

Docs

  • docs/interpose.md: multicast objects restore only on a user-mode driver
    of r595 or newer (ai-dynamo/snapshot#510); the image's forward-compatible
    libcuda counts, the host kernel driver may stay on 580.
  • The hardware table has a b300-275gb row (NVIDIA B300 SXM6, sm_103).