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dlewis1788

HN member
Joined
Sun, Jan 21, 2018, 3:52 AM UTC
HN karma
67
Public activity
13 items

About dlewis1788

CTO, Silex Data Solutions Opinions expressed here are my own.

LinkedIn - https://www.linkedin.com/in/derek-lewis-b3881318/ GitHub - https://github.com/derekelewis Twitter - https://twitter.com/derekelewis

Recent public activity

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    Comment #44261017

    Looks like more than KV is having an issue. Just tried to load dash.cloudflare.com and no bueno.

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    Comment #43548948

    100% - probably why vLLM is now the default back-end in Dynamo.

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    Comment #43548849

    100% valid - Nvidia is trying to address that now with cuTile and the new Python front-end for CUTLASS.

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    Comment #43547586

    CUDA is an entire ecosystem - not a single programming language extension (C++) or a single library, but a collection of libraries & tools for specific use cases and optimizations …

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    Comment #43407299

    Just curious what your issues with Triton were. We've done OK with it using it to serve LLM models w/ a classifier head via HF Transformers pipeline & Flash Attention 2, as well as…

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    Comment #41153663

    For training, yes, but no indications on inference workloads. Apple has said they would use their own silicon for inference in the cloud.

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    Comment #36576872

    Someone commented below that with enough batchnorm/layernorm/etc. and/or gradient clipping you can manage it, but BF16 just makes life easier if you can live without some precision…

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    Comment #36576804

    I didn't even know about Apple's AMX instructions until I clicked on your link. Very interesting - thanks!

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    Comment #36576739

    My understanding is for certain types of networks BF16 will train better than FP16, given the additional protection against exploding gradients and loss functions with the extended…

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    Comment #36576673

    Confirmed Apple M1 lacks bfloat16 support completely - M1: hw.optional.arm.FEAT_BF16: 0 vs M2: hw.optional.arm.FEAT_BF16: 1

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    Comment #36575444

    Somehow missed this from WWDC23, but it looks like Sonoma will add support for bfloat16 with Metal, and there's an active PR to add support with the PyTorch MPS back-end (PR #99272…

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