@seclink: Fun fact: Mamba camp: taking the "cloud-side frontier model" route, with customers like NVIDIA/AI21/IBM — companies that sell enterprise AI platforms → If you want to do cloud API data processing for long-context, low-cost inference, this direction is worth tracking. RWKV camp: taking the "on-device/edge embedded…
Summary
The article compares the routes of two AI model architecture camps, Mamba and RWKV: Mamba targets cloud-side enterprise AI platforms, while RWKV targets on-device and edge devices, each adapting to different hardware and inference scenarios.
View Cached Full Text
Cached at: 08/14/26, 01:39 PM
Fun fact:
The Mamba camp: takes the “cloud-side frontier model” route, with customers like NVIDIA/AI21/IBM — companies selling enterprise AI platforms. → If you’re working on long-context, low-cost inference cloud API data processing, this direction is worth tracking.
The RWKV camp: takes the “on-device/edge-embedded” route, with customers like chipmakers, robotics companies, and operating system vendors. → If you’re planning to build local real-time inference data loops for on-device dexterous hands / first-person-view devices in the future, RWKV’s constant memory footprint and KV-cache-free design actually fit your hardware constraints better.
Similar Articles
@MaxForAI: http://Z.ai and this ZCube paper from Tsinghua—worth a read for anyone in Infra. Many people's first reaction when talking about AI infra is still GPU, memory, quantization, and inference frameworks. But once you get into long context and Prefill-Decode separation, the network is no longer just a 'supporting role' in the data center. Every...
ZCube is a new network architecture that flattens the topology and mixes single/multi-rail access to optimize KV Cache transmission in long-context and PD separation scenarios. In the GLM-5.1 production cluster, it achieved a 33% reduction in switch/optical module costs, a 15% increase in GPU inference throughput, and a 40.6% decrease in TTFT P99.
@seclink: 1. Dell + NVIDIA launch Agentic AI full-stack solution (highest impact) - Event: Dell and NVIDIA launch 'AI Factory' + desktop-grade Agentic AI product line, covering everything from local workstations to liquid-cooled racks - Key data...
Dell and NVIDIA jointly released an Agentic AI full-stack solution, including the AI Factory and a desktop-grade product line, covering from local workstations to liquid-cooled racks, aimed at reducing the cost and latency of routing AI inference workloads to the public cloud.
@LinQingV: When exploring LLM inference chip architectures previously, I reviewed the architectures of the four major AI inference ASIC companies: Groq, SambaNova, Tenstorrent, and Cerebras. While the first three have different emphases, their underlying logic falls within the same framework: large on-chip SRAM + dataflow architecture + deterministic scheduling...
The article analyzes the AI inference ASIC architectures of Groq, SambaNova, Tenstorrent, and Cerebras, highlighting Cerebras's unique wafer-scale engine design. It discusses the benefits of deterministic latency and high bandwidth for LLM inference, while noting challenges like yield, cost, and KV cache bottlenecks.
@QingQ77: A self-deployable agentic AI + BI platform: build agents, run multi-agent swarms, connect knowledge bases and data sources, view complete execution traces — all running on your own machine and your own API keys. https://github.com/AgentSwa…
Introducing AgentSwarms, a self-deployable agentic AI and BI platform that supports building agents, running multi-agent swarms, connecting knowledge bases and data sources, and viewing complete execution traces. Your data and API keys remain in your hands.
@seclink: Fun fact: Currently, the specific implementation directions for multimodal large model startups typically include the following. If none of these interest you, don't follow the trend and go back to learning AI coding: 1. Game AI NPC / Agent middleware (e.g., end-cloud collaborative OmniNPC, empowering 3D character interaction and emotional storytelling...)
Summarizes several main implementation directions for current multimodal large model startups, including game AI NPC, enterprise-level multimodal Agent, content generation, embodied intelligence, and visual code assistants.