Comment by janalsncm
11 hours ago
PTQ and vector quantization aren’t used for this because part of the point of ternary LLMs is to make them faster. In a ternary LLM every weight is an add, subtract, or no-op so it is fast on CPU.
If you’re just using a code book to reconstruct a f16 model the only savings you can get are in sending it over the wire.
And storing it in memory. Memory is expensive.
which is important though since sending it across the wire over and over and over is actually the main bottleneck.
Wire typically means internet connection, and it’s hardly the bottleneck
in the case of large language models, the wire is the communication of your parameters between your layers of memory that is often the bottleneck. To do a forward pass, you need to use all parameters once, and so the communication between the compute and the storage is the bottleneck, and that bottleneck is also a bunch of wires.
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> If you’re just using a code book to reconstruct a f16 model the only savings you can get are in sending it over the wire.
That’s why you need to use efficient gemm kernels like FLUTE for inference. They are ~as good as what you can do with ternary quantization.