If we want hardware acceleration for smaller types, we'll need to use small integers w/ vectorized instruction sets. Going beyond 16-bit down to 8 or 4 bits is possible, but not with hardware accelerated floating point operations. It's been used by both algorithms and artists for more than a hundred years. Quantizationĭiscrete quantization is not a new idea. If we're building dynamic user experiences, it's worth digging deeper into optimizations. ]Ĥ.5 seconds is slow for an interactive response. Most research has shown this is a good quality/performance tradeoff, and that model outputs are not terribly sensitive when truncating the least significant bits. There are a couple competing 16-bit standards, but NVIDIA has introduced support for bfloat16 in their latest hardware generation, which keeps the full exponential range of float32, but gives up a 2/3rs of the precision. One way to reduce the amount of RAM and memory bandwidth needed is to use a smaller datatype, like 16-bit floating point numbers, which would reduce the model size in RAM by half. This is a lot of RAM, and it's not even counting what's needed to store the input and output data.īandwidth between RAM and CPU often becomes a bottleneck for performing inference with these models, rather than the number of processing cores or their speed, because the processors become starved for data. The Leading Open Source models like LLaMA, Alpaca, and Guanaco, currently have 65B parameters, which requires about 260GB RAM. Typically, these parameters are represented by individual 32-bit floating point numbers, so a model like GPT-2 that has 1.5B parameters would need 4 bytes * 1,500,000,000 = 6GB RAM. They have a lot of parameters, which make up the weights and biases of the layers inside deep neural networks. Half-precision floating point and quantized optimizations are now available for your favorite LLMs downloaded from Huggingface. These algorithms perform inference significantly faster on NVIDIA, Apple and Intel hardware. Quantization allows PostgresML to fit larger models in less RAM.
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