Cisco Network Convergence System 6000 Series Routers
The Network Convergence System (NCS) 6000 helps enable superior network agility, packet optical convergence, and petabits-per-second system scale. It facilitates the Cisco Evolved Programmable Network to support virtualization and programmability at low total cost of ownership and delivers high-bandwidth mobile, video, and cloud services to end users. Innovations include Cisco nPower X1 NPUs, hardware-enabled true zero-packet, zero-topology loss ISSU, capability to scale beyond 1 petabit in a multi-chassis configuration, enhanced operations support, and packet-optical integration. Use an adaptable power consumption model for both the ASIC and CMOS photonics technology for the lowest carbon footprint in service provider routing today. Easily adapt each line card's power consumption to the number of ports used.
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Cisco 8000 Series Routers
CiscoĀ® 8000 Series routers complete this journey. They deliver provider-class routing functionality at unmatched density, performance, and power. This enables Cisco 8000 Series to be deployed into an unprecedented range of routing roles ā all supported with a single ASIC architecture and operating system ā thus streamlining qualification, deployment, and operations. The Cisco 8000 Series combines the revolutionary Cisco Silicon Oneā¢, IOS XRĀ® software, and a set of clean sheet chassis to deliver a breakthrough in high-performance routers. The 8000 Series comprises a full range of feature-rich, highly scalable, deep-buffered, on-chip High Bandwidth Memory (HBM) and 400 Gigabit Ethernet (GbE)-optimized routers ranging from 10.8 to 25.6 Tbps in a 1 RU footprint. It is also available in an industry leading, rack-mountable modular system capable of 518.4 Tbps of full-duplex, line rate forwarding.
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vLLM
vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more.
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Wafer
Wafer delivers the fastest open source LLMs for enterprise through serverless and dedicated inference built for production AI workloads. Its serverless inference gives teams access to top open models with no infrastructure, no deployment overhead, and fast APIs, including GLM-5.2-Fast for low-latency inference with EAGLE speculative decoding and a per-stream throughput SLA, GLM-5.2 as a flagship model with stronger coding and reasoning capabilities, and more. Waferās technology uses agents that optimize inference across the stack, identifying and enhancing bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and diverse hardware. It profiles the stack to see whether latency or throughput comes from scheduling, decoding, kernels, memory pressure, or hardware fit, then tries many paths and ships the measured winner. Instead of relying on a single switch or heuristic, Wafer searches model, engine, kernel, and hardware combinations.
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