Yes, this is correct. For Triton we've been focusing mostly on server GPUs, so V100 and A100. It's probably not much work to get it to work on M60 and K80 though. I'll leave this open in case anyone wants to take up this issue :)

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Hi! It is possible for sure. Where are you getting the error specifically? You'd just need to edit the autotuner's config so it doesn't use anything that your hardware doesn't support

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@ptillet Thanks a lot. This issue happens at below code:
line 551 of /python/triton/code_gen.py

   `tt_device = _triton.driver.cu_device(device.index, False)
    # Compile to machine code
    mod, ker, shared_mem, ir_asm = _triton.code_gen.add_passes_to_emit_bin(generator.module, tt_device, num_warps, num_stages)

    if shared_mem > tt_device.max_shared_memory():
        raise  OutOfResources(shared_mem, tt_device.max_shared_memory(), "shared memory")

    return Binary(mod, ker, num_warps, num_stages, shared_mem, ir_asm)`

I tried on M60&K80. tt_device.max_shared_memory() is 49152 and has this issue. For V100. seems like it's enough

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Hi, @ptillet

I find the following information from the readme. Seem that triton only supports GPU with Compute Capability >= 7.0?
The compute capability of M60 is 5.2 and K80 is 3.7

Compatibility

Supported Platforms:

• Linux

Supported Hardware:

• NVIDIA GPUs (Compute Capability 7.0+)
• Under development: AMD GPUs, CPUs

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I'm guessing K80 support is for Google Colab. They have been providing T4 more (anecdotally), last 4 notebooks I got were all T4 so maybe just asking for a new kernel if you get a K80 is workable. T4 worked just fine with Triton and got some modest speedups on the vector add tutorial at large sizes

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