AMD Instinct MI355X Benchmark Results for AI Inference

AMD has released internal benchmark results for its new Instinct MI355X GPU. The tests demonstrate how the card handles large language model inference – both solo and when working in tandem with other accelerators.

Tests conducted and scenarios

What Was Tested

The company tested the MI355X in two scenarios. The first was single-node operation, meaning the entire model runs on one or multiple cards within a single server. The second was distributed inference, where the model is split across multiple servers that exchange data over a network.

In simpler terms, the first case involves installing the card in a standard server and running the model. The second applies when the model is too large or requires high bandwidth, so it is distributed across several machines.

MI355X benchmark performance results

The Results Proved Competitive

AMD reports that the MI355X delivered competitive, and in some cases, superior results. While they provide the exact figures and comparison details in the benchmarks themselves, the key takeaway is that the card handles inference tasks at a level sufficient for industrial use.

This development is significant because the market for AI accelerators is no longer dominated by a single manufacturer. The more options available with acceptable performance, the broader the choices for those building infrastructure for models.

Importance of inference performance in AI

Why This Matters

Inference occurs when a model has already been trained and begins working with real data. While training might be done once, inference happens constantly: every time a user sends a request to the model.

Therefore, inference performance directly impacts the number of requests that can be processed, how quickly the model responds, and the amount of hardware required to do so. The more efficient the card, the fewer servers are needed for the same workload.

Implications for the AI hardware industry

What This Means for the Industry

The MI355X is positioned as a solution for those deploying large models in industrial operations. If the results are confirmed in practice by various customers, this could strengthen AMD's position in the AI accelerator market.

For those selecting hardware, this presents another viable option – especially for those working with distributed systems or seeking an alternative to established solutions.

AMD has published the full results and testing methodology on its developer website.