  Large deep learning models offer significant accuracy gains, but training
billions to trillions of parameters is challenging. Existing solutions such as
data and model parallelisms exhibit fundamental limitations to fit these models
into limited device memory, while obtaining computation, communication and
development efficiency. We develop a novel solution, Zero Redundancy Optimizer
(ZeRO), to optimize memory, vastly improving training speed while increasing
the model size that can be efficiently trained. ZeRO eliminates memory
redundancies in data- and model-parallel training while retaining low
communication volume and high computational granularity, allowing us to scale
the model size proportional to the number of devices with sustained high
efficiency. Our analysis on memory requirements and communication volume
demonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters
using today's hardware.
  We implement and evaluate ZeRO: it trains large models of over 100B parameter
with super-linear speedup on 400 GPUs, achieving throughput of 15 Petaflops.
This represents an 8x increase in model size and 10x increase in achievable
performance over state-of-the-art. In terms of usability, ZeRO can train large
models of up to 13B parameters (e.g., larger than Megatron GPT 8.3B and T5 11B)
without requiring model parallelism which is harder for scientists to apply.
Last but not the least, researchers have used the system breakthroughs of ZeRO
to create the world's largest language model (Turing-NLG, 17B parameters) with
record breaking accuracy.
