  We report on aggressive quantization strategies that greatly accelerate
inference of Recurrent Neural Network Transducers (RNN-T). We use a 4 bit
integer representation for both weights and activations and apply Quantization
Aware Training (QAT) to retrain the full model (acoustic encoder and language
model) and achieve near-iso-accuracy. We show that customized quantization
schemes that are tailored to the local properties of the network are essential
to achieve good performance while limiting the computational overhead of QAT.
  Density ratio Language Model fusion has shown remarkable accuracy gains on
RNN-T workloads but it severely increases the computational cost of inference.
We show that our quantization strategies enable using large beam widths for
hypothesis search while achieving streaming-compatible runtimes and a full
model compression ratio of 7.6$\times$ compared to the full precision model.
  Via hardware simulations, we estimate a 3.4$\times$ acceleration from FP16 to
INT4 for the end-to-end quantized RNN-T inclusive of LM fusion, resulting in a
Real Time Factor (RTF) of 0.06. On the NIST Hub5 2000, Hub5 2001, and RT-03
test sets, we retain most of the gains associated with LM fusion, improving the
average WER by $>$1.5%.
