--- 格式版本: 2 标题: "Intel, SambaNova Bet on Split Inference as Agentic AI Strains GPUs" 原文链接: "https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus" 发布日期: "2026-08-11" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "manual" 发布时间证据: "正文中靠近标题处明确标注Aug 11, 2026，且为local:strict_original_body来源，优先级最高，无其他元数据干扰。" 发布时间校准原因: "正文中靠近标题处明确标注Aug 11, 2026，且为local:strict_original_body来源，优先级最高，无其他元数据干扰。" 发布时间校准置信度: "manual-confirmed" 发布时间候选数量: 0 发布时间严格候选数量: 0 发布时间原页读取状态: "manual" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-18T07:03:02+08:00" 发布时间仲裁状态: "manual" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-08-12T03:57:51+08:00" 入库时间: "2026-08-11T20:06:57.806Z" 来源平台: "Data Center Knowledge 搜索" 搜索渠道: "source_template" 搜索词: "https://www.datacenterknowledge.com/search?q=model%20fit" 匹配关键词: - "model fit" - "AI" - "GPU" - "Liquid Cooling" - "performance" - "latency" - "bandwidth" 相关厂家: - "NVIDIA" - "AMD" - "AWS" - "Google" 相关专家: [] 内容类型: "网页" 抓取工具: "Jina Reader" 清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 35 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "文章讨论split inference架构，但未涉及超节点、AI Rack、机柜级系统、高速互连、供电、液冷等核心主题，缺乏技术细节和商业部署信号，属于泛泛的AI软件架构新闻。" AI质检模型: "tx-deepseek-v4-flash" AI质检时间: "2026-08-12T04:07:07+08:00" AI主题相关性: 5 AI来源权威性: 10 AI新颖性: 12 AI技术细节: 5 AI商业部署信号: 2 AI完整性: 1 采集批次: "2026年8月12日2点04分20秒" 采集批次ID: "20260812-020420-289" 去重键: "https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus" --- Title: Intel, SambaNova Bet on Split Inference as Agentic AI Strains GPUs URL Source: https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus Published Time: 2026-04-08T12:00:00.000Z Markdown Content: [![Image 1: Informa Logo](https://www.datacenterknowledge.com/assets/informaLogoCross-SPlaD0EU.png)Informa TechTarget|](https://www.informatechtarget.com/) [AI Business](https://aibusiness.com/ "AI 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architecture that pairs GPUs, RDUs, and CPUs to improve efficiency and scalability as agentic AI workloads expose the limits of GPU-only systems. [![Image 17: Picture of Shane Snider](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt68d42dde3032e1e9/67212dce29bb88408c6ac9be/Shane_Snider_Profile.webp?width=100&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/shane-snider) [Shane Snider,](https://www.datacenterknowledge.com/author/shane-snider)Senior News Writer,Data Center Knowledge April 8, 2026 4 Min Read ![Image 18](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blteb640fc25dbb9002/69d54c17a733fd167fcb940c/chip_1.jpg?width=1280&auto=webp&quality=80&disable=upscale) Image: SambaNova [](http://www.facebook.com/sharer/sharer.php?u=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](https://www.linkedin.com/sharing/share-offsite/?url=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](http://www.twitter.com/intent/tweet?url=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](https://www.reddit.com/submit?url=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus&title=Intel%2C%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs)[](https://bsky.app/intent/compose?text=Intel%2C%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs%20-%20https%3A%2F%2Fwww.datacenterknowledge.com%2Fdata-center-chips%2Fintel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](mailto:?subject=Intel,%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs&body=I%20thought%20the%20following%20from%20DataCenterKnowledge%20might%20interest%20you.%0D%0A%0D%0A%20Intel%2C%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs%0D%0Ahttps%3A%2F%2Fwww.datacenterknowledge.com%2Fdata-center-chips%2Fintel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus) SambaNova and Intel are rolling out a new heterogeneous inference architecture aimed at a growing pressure point in enterprise AI: running agentic workloads at scale without over-relying on GPUs. The design splits inference across three layers – GPUs for prefill, SambaNova’s reconfigurable dataflow units (RDUs) for decode, and Intel Xeon 6 CPUs for agent execution and orchestration – reflecting a broader shift away from monolithic GPU systems. The push comes as enterprises run into rising inference costs, power constraints, and underutilized GPU capacity, forcing a rethink of GPU-heavy designs. The offering, slated for availability in the second half of 2026, targets enterprises, cloud providers, and sovereign AI deployments building coding agents and other multi-step systems. ## The Bottleneck Shifts From Training to Execution The premise is less about replacing GPUs and more about where they fall short. GPUs remain effective at parallel prompt processing, but agentic AI introduces workloads that are less uniform and more dependent on coordination, tool use, and real-time decision-making. Related:[‘GPUs Suck’: Former Intel CEO Slams Data Center Hardware Limitations](https://www.datacenterknowledge.com/data-center-chips/gpus-suck-former-intel-ceo-slams-data-center-hardware-limitations) “Agentic AI is moving into production – and the winning pattern we’re seeing is GPUs to start the job, Intel Xeon 6 to run it, and SambaNova RDUs to finish it fast,” said Rodrigo Liang, CEO of SambaNova, in prepared remarks. Coding agents are a clear example. They compile and execute code, call APIs, query databases, and coordinate workflows continuously. Those steps lean heavily on CPUs and memory bandwidth, pushing the CPU into a central role in the inference pipeline. “GPUs are very good at parallelizing matrix math for input processing. They’re not good at decoding, especially when you have latency-sensitive workloads,” said Anton McGonnell, vice president of product at SambaNova. Industry analysts broadly agree the shift is underway, though not settled. “We have reached the point where heterogeneous compute is the way to go,” said Patrick Moorhead, CEO and chief analyst at Moor Insights & Strategy. “We need to get more efficient and therefore the right compute for the right task, be it training, prefill, decode and agentic orchestration.” That shift marks a transition from training-centric infrastructure to inference systems optimized for latency, orchestration, and cost. ## A Three-Tier Inference Pipeline The architecture divides inference into distinct stages, with each processor type handling a different part of the workflow. GPUs process prompts and generate key-value caches, RDUs handle token generation during decode, and CPUs manage orchestration and execution of agent tasks. Related:[AMD Fires Back at Nvidia with Helios AI System, Epyc CPUs](https://www.datacenterknowledge.com/data-center-chips/amd-fires-back-at-nvidia-with-helios-ai-system-epyc-cpus) The underlying argument is straightforward: no single processor type is optimal across all phases, particularly as inference becomes more interactive and stateful. “The main thing is making sure people are getting full utilization of the assets that they purchased,” said Jeff McVeigh, vice president and general manager of data center strategic programs at Intel. That framing shifts the conversation from peak performance to system efficiency – keeping each stage of inference fully utilized rather than overloading a single processor type. SambaNova’s RDUs are positioned as the key component in the decode phase, where token generation speed and efficiency directly shape latency and cost. Intel, for its part, is leaning on ecosystem gravity. “The data center software ecosystem is built on x86,” said Kevork Kechichian, EVP and general manager of Intel’s Data Center Group, framing Xeon as a stable foundation for enterprise deployments. ## Efficiency Comes With Complexity The companies are not claiming outright performance wins over GPU-only systems. The pitch is efficiency – better utilization, lower cost per workload, and more balanced systems. “One size obviously does not fit all. You need optimization for every stage of the workflow,” McVeigh said. Related:[Europe’s Chips Act: A Brief Supply Chain Opinion](https://www.datacenterknowledge.com/data-center-chips/europe-s-chips-act-a-brief-supply-chain-opinion) The trade-off, according to Moorhead, is a more complex but more efficient system. “The key is to have a simplified layer of software that spans the workloads to reduce the complexity,” he said. That software layer is the hinge point. Without it, distributing workloads across GPUs, RDUs, and CPUs risks introducing operational overhead that offsets any efficiency gains. SambaNova cited internal benchmarks showing Xeon 6 improving compilation times and vector database performance, though those figures are based on company testing and have not been independently verified. ## What to Watch Heterogeneous inference is not new. Hyperscalers already distribute workloads across CPUs, GPUs, and custom accelerators. What Intel and SambaNova are trying to do is package that model into a repeatable blueprint for enterprise buyers. The open questions are execution. RDUs must prove competitive on cost and ecosystem maturity, and enterprises must see measurable efficiency gains. Added to this, the software layer must make the system usable, not just possible. With availability pushed to the second half 2026, this is a forward-looking bet from Intel and SambaNova on how agentic AI workloads evolve. [](http://www.facebook.com/sharer/sharer.php?u=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](https://www.linkedin.com/sharing/share-offsite/?url=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](http://www.twitter.com/intent/tweet?url=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](https://www.reddit.com/submit?url=https://www.datacenterknowledge.com/data-center-chips/intel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus&title=Intel%2C%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs)[](https://bsky.app/intent/compose?text=Intel%2C%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs%20-%20https%3A%2F%2Fwww.datacenterknowledge.com%2Fdata-center-chips%2Fintel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus)[](mailto:?subject=Intel,%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs&body=I%20thought%20the%20following%20from%20DataCenterKnowledge%20might%20interest%20you.%0D%0A%0D%0A%20Intel%2C%20SambaNova%20Bet%20on%20Split%20Inference%20as%20Agentic%20AI%20Strains%20GPUs%0D%0Ahttps%3A%2F%2Fwww.datacenterknowledge.com%2Fdata-center-chips%2Fintel-sambanova-bet-on-split-inference-as-agentic-ai-strains-gpus) ## About the Author [![Image 19: Shane Snider](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt68d42dde3032e1e9/67212dce29bb88408c6ac9be/Shane_Snider_Profile.webp?width=400&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/shane-snider) [Shane Snider](https://www.datacenterknowledge.com/author/shane-snider) Senior News Writer, Data Center Knowledge Shane Snider is Senior News Writer at Data Center Knowledge, covering AI infrastructure, hyperscale data centers, cloud platforms, and the power and energy systems driving modern compute expansion. His reporting focuses on the operational, economic, and environmental forces reshaping digital infrastructure, including AI factories, utility constraints, liquid cooling, renewable energy procurement, and next-generation data center architectures. He has won recent Azbee awards for news series and government reporting. Based in Raleigh, North Carolina, Snider covers how hyperscalers, utilities, chipmakers, and infrastructure providers are responding to the rapid rise of AI workloads and global compute demand. You can reach Shane at [shane.snider@informatechtarget.com](mailto:shane.snider@informatechtarget.com) or on [LinkedIn](https://www.linkedin.com/in/shane-snider-1727755/). 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