ICML 2025

Hardware and Software Platform Inference
Cheng Zhang * 1 Hanna Foerster * 2 Robert D. Mullins 2 Yiren Zhao 1 Ilia Shumailov 3
Abstract
It is now a common business practice to buy ac-
cess to large language model (LLM) inference
rather than self-host, because of significant up-
front hardware infrastructure and energy costs.
However, as a buyer, there is no mechanism
to verify the authenticity of the advertised ser-
vice including the serving hardware platform, e.g.
that it is actually being served using an NVIDIA
H100. Furthermore, there are reports suggest-
ing that model providers may deliver models that
differ slightly from the advertised ones, often
to make them run on less expensive hardware.
That way, a client pays premium for a capable
model access on more expensive hardware, yet
ends up being served by a (potentially less capa-
ble) cheaper model on cheaper hardware. In this
paper we introduce hardware and software plat-
form inference (HSPI) – a method for identify-
ing the underlying GPU architecture and software
stack of a (black-box) machine learning model
solely based on its input-output behavior. Our
method leverages the inherent differences of var-
ious GPU architectures and compilers to distin-
guish between different GPU types and software
stacks. We evaluate HSPI against models served
on different real hardware and find that in a white-
box setting we can distinguish between differ-
ent GPUs with between 83.9% and 100% accu-
racy. Even in a black-box setting we achieve
results that are up to 3× higher than random
guess accuracy. Our code is available at https:
//github.com/ChengZhang-98/HSPI.
*Equal contribution
1Department of Computing, Impe-
rial College London, London, United Kingdom 2Department
of Computer Science and Technology, University of Cam-
bridge, Cambridge, United Kingdom 3Google DeepMind, Lon-
don, United Kingdom.
Correspondence to:
Cheng Zhang
<cheng.zhang122@imperial.ac.uk>.
Proceedings of the 42 nd International Conference on Mach...