  The demand for inference on extremely large scale LLMs has seen enormous
growth in the recent months. It made evident the colossal shortage of dedicated
hardware capable of efficient and fast processing of the involved compute and
memory movement. The problem is aggravated by the exploding raise in the
lengths of the sequences being processed, since those require efficient on-chip
storage of the KV-cache of size proportional to the sequence length. To make
the required compute feasible and fit the involved data into available memory,
numerous quantization techniques have been proposed that allow accurate
quantization for both weights and activations. One of the main recent
breakthroughs in this direction was introduction of the family of Block
Floating Point (BFP) formats characterized by a block of mantissas with a
shared scale factor. These enable memory- power-, and compute- efficient
hardware support of the tensor operations and provide extremely good
quantization accuracy. The main issues preventing widespread application of
block formats is caused by the presence of outliers in weights and activations
since those affect the accuracy of the other values in the same block. In this
paper, we focus on the most critical problem of limited KV-cache storage. We
propose a novel approach enabling usage of low precision BFP formats without
compromising the resulting model accuracy. We exploit the common channel-wise
patterns exhibited by the outliers to rearrange them in such a way, that their
quantization quality is significantly improved. The methodology yields 2x
savings in the memory footprint without significant degradation of the model's
accuracy. Importantly, the rearrangement of channels happens at the compile
time and thus has no impact on the inference latency.
