Deploy validated AI infrastructure faster. Cisco engineers and measures the silicon-to-workload stack as one system to uncover bottlenecks, optimize designs, and reduce risk.
AI performance lives beyond the XPU
Make every AI investment count
Measure what matters for your workload. Know what your AI environment can deliver—and where to invest, tune, or scale next.
Uncover your true AI TCO
Compare infrastructure requirements, efficiency, and model responsiveness across platforms. See how predictable performance shapes deployment needs and total cost.
Optimize AI performance
Tune AI with end-to-end metrics—from graphics processing unit (GPU) utilization and cluster throughput to tokens per second and latency—across compute, networking, storage, and software for modern AI applications.
Accelerate time to value
Cisco models tests to your AI use cases with industry-standard benchmarks, validating infrastructure before deployment—removing guesswork so you can focus on strategic priorities.
Explore AI benchmarking resources
Get practical insight from Cisco technical papers, published results, and explanatory videos.
Benchmark mixed accelerators
See Cisco's distributed inference service in its first multi-vendor MLPerf Inference submission.
Proven Ethernet for AI
Explore Cisco's rigorous testing of AI fabrics for performance, congestion, and workload behavior.
Validate scale-out fabrics
Learn how Cisco validates AI fabrics with AMD GPUs and network interface cards (NICs) across topologies and congestion scenarios.
Scale-across AI globally
Learn why distributed AI needs performance validation across sites, distance, and network domains.
AI benchmarking methodology
Discover workload-first methods and key metrics, from network baselines to full-scale AI workloads.
Scale AI with Cisco Silicon One G300
See how collective intelligent networking can improve communication time and AI training performance.
Inside Cisco's AI lab
See how engineers test, benchmark, and optimize AI clusters with real conditions and use cases.
Scale-Across at production scale
Learn how Cisco and Keysight use workload-based testing results to validate networks for scale-across AI.
Scale-Across at production scale
Industry's first
"Our MLPerf™ Inference v6.1 results demonstrate that Cisco Silicon One Ethernet can connect NVIDIA H200 and AMD Instinct™ MI350X systems with minimal cross-architecture overhead. The combined service delivered up to 104.7% of their standalone performance."
Will Eatherton, SVP, Engineering, Data Center and Internet Infrastructure
MLPerf™ Inference
Explore data center inference results across models and deployment scenarios.