A Hardware Evaluation Framework for Large Language Model Inference
Hengrui Zhang August Ning Rohan Prabhakar David Wentzlaff
The past year has witnessed the increasing popularity of Large Language
Models (LLMs). Their unprecedented scale and associated high hardware cost have
impeded their broader adoption, calling for efficient hardware designs. With
the large hardware needed to simply run LLM inference, evaluating different
hardware designs becomes a new bottleneck.
  This work introduces LLMCompass, a hardware evaluation framework for LLM
inference workloads. LLMCompass is fast, accurate, versatile, and able to
describe and evaluate different hardware designs. LLMCompass includes a mapper
to automatically find performance-optimal mapping and scheduling. It also
incorporates an area-based cost model to help architects reason about their
design choices. Compared to real-world hardware, LLMCompass' estimated latency
achieves an average 10.4% error rate across various operators with various
input sizes and an average 4.1% error rate for LLM inference. With LLMCompass,
simulating a 4-NVIDIA A100 GPU node running GPT-3 175B inference can be done
within 16 minutes on commodity hardware, including 26,400 rounds of the
mapper's parameter search.
  With the aid of LLMCompass, this work draws architectural implications and
explores new cost-effective hardware designs. By reducing the compute
capability or replacing High Bandwidth Memory (HBM) with traditional DRAM,
these new designs can achieve as much as 3.41x improvement in performance/cost
compared to an NVIDIA A100, making them promising choices for democratizing
LLMs.
  LLMCompass is planned to be fully open-source.