Arista Networks is an industry leader in data-driven, client to cloud networking for large data center, campus and routing environments. Arista’s award-winning platforms deliver availability, agility, automation, analytics and security through an advanced network operating stack.

1. The Next Frontier in AI Networking
The rapid arrival of real-time gaming, virtual reality and metaverse applications is changing the
way network, compute memory and interconnect I/O interact for the next decade. As the
future of metaverse applications evolve, the network needs to adapt for 10 times the growth in
traffic connecting 100s of processors with trillions of transactions and gigabits of throughput. AI
is becoming more meaningful as distributed applications push the envelope of predictable scale
and performance of the network. A common characteristic of these AI workloads is that they
are both data and compute-intensive. A typical AI workload involves a large sparse matrix
computation, distributed across 10s or 100s of processors (CPU, GPU, TPU, etc.) with intense
computations for a period of time. Once the data from all peers is received, it can be reduced or
merged with the local data and then another cycle of processing begins.
Historically, the only option to connect processor cores and memory have been proprietary
interconnects such as InfiniBand and other protocols that connect compute clusters with
offloads. However, the scale and richness of Ethernet radically changes the paradigm with
better distributed options now. Let's take a look at alternatives.
1. Ethernet NICs and Switches
Smart or high performance NICs often interconnect the sea of multiple cores in a network
design. This is an emerging trend where the Network interface Controller (NIC) not only
provides network connectivity but drives server offloads. The traditional design philosophy is to
leverage general purpose GPU or DPU cores and interconnect with the right price/performance
across memory and processors with accelerators such as RDMA (Remote Direct Memory
Access). DMA is an operation to access the memory directly from the NIC without involving the
CPU. Today’s NICs connect to Ethernet 10/100/200G switches, complementing the NICs, using a
programmable framework often based on P4, such as the Arista 7170 series, as well as the 7050
series for more expanded memory and feature coverage.

2. 2. InfiniBand
InfiniBand based switches and HBA (Host Bus Adapters) combine general purpose DPUs and
GPUs to deliver consistent performance and can use RDMA offloads. Typical IB networks are
vendor specific closed systems in high performance compute (HPC) use cases. The access on the
responder throughput is limited by the InfiniBand (NIC and PCI). The low software dependency,
decreases latency for InfiniBand versus TCP/UDP performance. However, smarter improved
Ethernet switches and NICs also adopt non-TCP methods so the delta is narrowing between IB
and Ethernet. Historically, InfiniBand was implemented in large supercomputer clusters but the
high cost of scale-out and proprietary nature brings poor interoperability and limitations for AI
and compute intensive applications.
3. Ethernet-based Spine Fabric
The insatiable appetite for faster transfer latency and Ethernet as a preferred fabric between
these processors is growing. AI processing grows exponentially for self-driving cars, interactive
and autonomous gaming and virtual reality, mandating a scalable and reliable network.
Small packets with large flows make the Arista 7800 with EOS the ideal frontrunner
combination. Designed with cell based VOQ (Virtual Output Queuing), and deep buffer
architecture, the Arista 7800 is a flagship platform for high radix scale 100/200/400/800G
throughput across all ports for efficient packet spraying and congestion control techniques such
as ECN (Explicit Congestion Notification) and PFC (Priority Flow Control).
This new AI Spine delivers a balanced combination of low power, high performance/latency and
reliability. The combination of high- radix and throughput of 400/800/Terabit Ethernet speed
based on open standards is a winner! The future of AI applications requires more scale, state
and flow in switches while maintaining simple standards-based compute for rack-automated
networks. Compute intensive AI applications need open mainstream Ethernet fabric for
improved latency, scale and availability with predictable behaviors for distributed AI processing
and applications. Welcome to Arista’s data-driven network era powered by AI spines for next
generation cloud networking!