Thank you for the detailed feedback. I’d like to clarify and correct a few points, as some of the issues referenced have already been addressed or are based on outdated information.
Corrections and current versions
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PyTorch: That is true, error typo.
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FlashInfer: Spark is supported from FlashInfer 0.5.3 Release Release v0.5.3 · flashinfer-ai/flashinfer · GitHub . Latest version is FlashInfer 0.6.1 Release Release v0.6.1 · flashinfer-ai/flashinfer · GitHub
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Triton: Some issues are fixed here Triton 3.6.0, which can be found here:
https://github.com/triton-lang/triton/tree/release/3.6.x or in pytorchpip3 install torch==2.10.0 torchvision torchaudio triton --index-url https://download.pytorch.org/whl/test/cu130
https://download.pytorch.org/whl/test/triton/
Frameworks and wheels
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SGLang and vLLM: Both projects release wheels at a faster cadence than their docker stable tags. In addition to official releases, nightly wheels are available for SGLang, vLLM, and PyTorch, allowing users to validate the latest fixes and improvements as they land. From our side, we are working to bring new containers.
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CUTLASS: CUTLASS is compatible with DGX Spark starting from version 4.2.0, as documented in the official changelog: Release CUTLASS 4.2.0 · NVIDIA/cutlass · GitHub
Last version is 4.3.5 Release CUTLASS 4.3.5 · NVIDIA/cutlass · GitHub and upcoming version is v4.4.x. v4.4.x comes better cute DSL.
Regarding linked issues
Several of the issues referenced have already been resolved. In at least one case, the problem originated from a custom build taken directly from the main branch, which can introduce temporary or unrelated regressions. @shahizat
Additionally, SGLang is compatible, I am showing here: Run SGLang in Spark, vLLM too Run VLLM in Spark and the referenced thread is primarily a discussion rather than an indication of lack of support. I am on there.
We recommend tracking official releases and nightly builds for the most experimented developers or testing.