A brand new paradigm is reshaping cloud infrastructure: neoclouds. These AI-first next-gen cloud suppliers are constructing GPU-dense platforms designed for the unrelenting scale and efficiency calls for of recent machine studying. In contrast to conventional cloud suppliers retrofitting present infrastructure, neoclouds are purpose-building AI-native materials from the bottom up—the place each GPU cycle counts and each packet issues.
In these AI-native environments, the community is not a passive conduit. It’s the synchronizing power that retains colossal clusters of GPUs working at full throttle, each second of the day. Attaining this requires extra than simply bandwidth: it calls for deterministic, lossless operation, deep observability, and the agility to evolve as AI workloads and architectures shift.
The Neocloud blueprint: Open, scalable, and AI-optimized with Cisco 8000
That is the place the Cisco 8000 Sequence with SONiC steps in—not as a standard swap, however because the clever spine for neoclouds. Designed for a disaggregated, open networking strategy, the Cisco 8000 Sequence with SONiC straight addresses the distinctive wants of AI-native clouds in 4 basic methods:
1. Operational agility by means of disaggregation
The Cisco 8000 Sequence presents a versatile, open platform ideally suited for neoclouds looking for fast innovation. With absolutely supported Cisco-validated SONiC and key AI options, the platform allows a really disaggregated stack. This enables for unbiased {hardware} and software program updates, straightforward integration of open-source capabilities, and superior AI observability and site visitors engineering. For backend buildouts, the Cisco 8122-64EH-O (64x800G QDD) and 8122-64EHF-O (64x800G OSFP) platforms—each powered by the Cisco Silicon One G200 ASIC—ship high-performance 800G throughput to satisfy the wants of demanding AI and information heart workloads. These platforms mix dependable, purpose-built {hardware} with agile, cloud-native software program, guaranteeing a scalable basis for evolving infrastructure wants.
2. Deterministic, lossless cloth for distributed coaching
AI clusters rely upon synchronized, high-bandwidth, lossless networks to maintain 1000’s of GPUs absolutely utilized. The Cisco 8122 platforms, constructed with G200 ASICs, ship giant, absolutely shared, on-die packet buffer, ultra-low jitter, and adaptive congestion administration—all important for RDMA-based workloads and collective operations. With assist for 800G in the present day and 1.6T speeds tomorrow, the material can scale as quick as AI ambition grows.
3. Intelligence inbuilt: Superior AI networking options
Cisco’s providing is anchored by its superior AI networking options—a wealthy set of instruments designed to supply real-time community insights, workload-aware scheduling, and dynamic congestion isolation. These options allow the material to implement predictive site visitors steering, aligning community conduct with AI workload patterns to maximise cluster effectivity and throughput.
4. Open, programmable, and future-proof
With open NOS like SONiC, the community turns into as programmable because the AI workloads it helps. Operators can quickly deploy new options, combine with GPU schedulers, and prolong the telemetry pipeline to match evolving wants. Moreover, the Cisco 8122 platforms are UEC-ready, aligning with the rising Extremely Ethernet Consortium 1.0 requirements to make sure your community is ready for future AI calls for.
Scaling the AI supercloud: Out and throughout
Determine 1: Scale out and scale throughout
Scale out: Creating multi-tier backend AI materials with clever cloth capabilities
As AI workloads scale, it’s essential for the underlying community to advance in each bandwidth and intelligence. Cisco multistage Clos topologies, constructed with Cisco 8122 platforms, ship actually non-blocking materials optimized for large-scale GPU clusters. On the coronary heart of this resolution is the great, AI-native networking feature-set designed to maximise efficiency and effectivity for AI clusters.
Key capabilities embrace:
- Superior congestion administration:
Precedence Circulate Management (PFC) and Express Congestion Notification (ECN) work in tandem to make sure the best throughput and minimal latency throughout congestion, holding clusters synchronized and working easily. - Adaptive routing and switching (ARS):
Dynamically steers site visitors based on real-time congestion and movement patterns, maximizing effectivity throughout all the community cloth. ARS presents two sub-modes:- Flowlet load balancing: Splits site visitors into micro-bursts (flowlets) and routes every alongside the optimum path, bettering utilization whereas preserving packet order—important for RDMA-based GPU workloads.
- Packet spraying: Distributes packets throughout all obtainable paths for optimum throughput, ideally suited for AI collective operations that tolerate packet reordering.
- Weighted ECMP:
Visitors is distributed erratically over a number of equal-cost paths based on predefined weights. This ensures higher-capacity or less-congested hyperlinks carry extra site visitors, bettering general utilization and efficiency in large-scale deployments. - QPID hashing:
Employs superior hashing methods to evenly unfold site visitors, minimizing movement collisions and stopping single-path oversubscription. - Packet trimming:
Throughout excessive congestion, non-essential packet payloads are eliminated to alleviate hotspots, whereas essential header info is retained for continued routing with out dropping total packets. - Versatile topology assist:
Suitable with quite a lot of community architectures—together with rail-only, rail-optimized, and conventional leaf/backbone topologies. The system helps each IPv4 and IPv6 underlays and integrates with IP/BGP and EVPN-based materials, permitting operators to tailor networks to particular AI cluster wants. - Multivendor SmartNIC interoperability:
Designed for seamless integration with a various ecosystem of SmartNICs from a number of distributors, guaranteeing flexibility, funding safety, and future-proof infrastructure. - AI-driven observability with PIE port:
Gives deep, real-time visibility at each per-port and per-flow ranges—together with GPU-to-GPU site visitors and congestion hotspots—utilizing ASIC-level telemetry, in-band INT packet tracing, and SONiC integration. This allows operators to proactively monitor, tune, and troubleshoot networks to optimize AI coaching outcomes.
Collectively, these options create a material that isn’t solely extremely scalable but in addition actually AI-optimized. The Cisco 8122 platforms’ clever networking capabilities allow the community to soak up synchronized site visitors bursts, forestall congestion collapse, and maintain each GPU working at peak effectivity—empowering next-generation AI workloads with unmatched efficiency and reliability.
Scale throughout: Federating AI pods globally
As AI infrastructure expands past single information facilities to span areas and continents, scale-across networking turns into essential. Neoclouds have to federate distributed GPU clusters whereas sustaining the low-latency, high-bandwidth efficiency that AI workloads demand.
The Cisco 8223, powered by Silicon One P200—the trade’s first 51.2T deep-buffer router—addresses this problem head-on. With built-in MACsec safety, 800GE interfaces supporting each OSFP and QSFP-DD optics, and coherent optics functionality, the 8223 delivers the flexibleness and effectivity next-generation distributed AI workloads require.
Native SONiC assist allows seamless integration between AI backends and WAN connectivity, permitting operators to construct open, programmable networks that scale globally with out sacrificing the efficiency traits of native clusters.
Accelerating neocloud AI networks with Cisco 8000 Sequence
Determine 2: Cisco 8000 Sequence for scale out and scale throughout
Within the AI period, networks have developed from infrastructure price facilities to aggressive differentiators. For neoclouds, networking efficiency straight impacts GPU utilization, coaching effectivity, and finally, buyer success.
By combining the Cisco 8000 Sequence platforms, superior AI networking options, and the openness of SONiC, neoclouds can construct infrastructure that scales seamlessly, operates effectively, and adapts as AI workloads evolve. It’s not nearly holding tempo with AI innovation—it’s about enabling it.
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