The smallest possible float size that follows all IEEE principles, including normalized numbers, subnormal numbers, signed zero, signed infinity, and multiple NaN values, is a 4-bit float with 1-bit sign, 2-bit exponent, and 1-bit mantissa.
I guess I was slightly off. What's even the point of this?
I guess I'm not an AI dev but this doesn't seem like enough values to be worth anything. Even 8-bit floats are pushing it but they have 16x as many distinct values (okay maybe not quite that much since there will be more NaN sequences but close).
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FP16 -> FP8 -> FP4
Who made this shit graph?
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next generation everything will be 1 bit floats
you can have zero or infinity
nothing will get done but it will get done faster than ever
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wtf even are 4-bit floats
like do they jettison the sign bit, or do they have 1 sign bit, 1 exponent bit, and 2 mantissa bits?
https://en.wikipedia.org/wiki/Minifloat
I guess I was slightly off. What's even the point of this?
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Speed I guess, can process more fp4 ops with each gpu and if you can say you have a larger model cause all the weights have less precision
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I guess I'm not an AI dev but this doesn't seem like enough values to be worth anything. Even 8-bit floats are pushing it but they have 16x as many distinct values (okay maybe not quite that much since there will be more NaN sequences but close).
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I'm not either but they have some models out there with 1 bit weights that seem to work (poorly)
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