---
格式版本: 2
标题: "Gimlet Labs, d-Matrix Partner to Boost Agentic AI Inference Performance"
原文链接: "https://www.datacenterknowledge.com/infrastructure/d-matrix-gimlet-labs-partner-to-boost-agentic-ai-inference-performance"
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: "llm:local:strict_original_body"
发布时间证据: "Aug 11, 2026"
发布时间校准原因: "正文标题附近明确标注 Aug 11, 2026，且为最晚日期，符合新闻发布时间特征。"
发布时间校准置信度: "1"
发布时间候选数量: 46
发布时间严格候选数量: 4
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T03:56:55+08:00"
发布时间仲裁状态: "confirmed"
发布时间仲裁尝试次数: 1
发布时间仲裁耗时毫秒: 82454
发现时间: "2026-08-12T03:53:36+08:00"
入库时间: "2026-08-11T19:58:17.526Z"
来源平台: "Data Center Knowledge 搜索"
搜索渠道: "source_template"
搜索词: "https://www.datacenterknowledge.com/search?q=latency"
匹配关键词:
  - "performance"
  - "latency"
  - "AI"
  - "GPU"
  - "Liquid Cooling"
  - "deployment"
  - "bandwidth"
  - "throughput"
相关厂家:
  - "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: 20
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "文章为AI推理芯片合作新闻，未涉及超节点/AI Rack/机柜级系统、高速互连、供电散热等核心主题，无技术细节或量产落地信息，与项目关注范围无关。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T03:58:26+08:00"
AI主题相关性: 2
AI来源权威性: 8
AI新颖性: 5
AI技术细节: 0
AI商业部署信号: 3
AI完整性: 2
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/infrastructure/d-matrix-gimlet-labs-partner-to-boost-agentic-ai-inference-performance"
---

Title: Gimlet Labs, d-Matrix Partner to Boost Agentic AI Inference Performance

URL Source: https://www.datacenterknowledge.com/infrastructure/d-matrix-gimlet-labs-partner-to-boost-agentic-ai-inference-performance

Published Time: 2026-03-12T13:00:00.000Z

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# Gimlet Labs, d-Matrix Partner to Boost Agentic AI Inference Performance

The companies say combining specialized inference accelerators with GPUs could deliver major speed and power efficiency gains for frontier AI workloads.

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[Shane Snider,](https://www.datacenterknowledge.com/author/shane-snider)Senior News Writer,Data Center Knowledge
March 12, 2026

4 Min Read

![Image 18: d-Matrix cosair](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/bltf26f2d3d0a1be068/69b1b8b3dbf0ad640702bd69/Corsair-Hero.jpg?width=1280&auto=webp&quality=80&disable=upscale)

d-Matrix

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AI infrastructure startup d-Matrix and applied AI firm Gimlet Labs have teamed up to bring specialized inference hardware into AI cloud environments, aiming to boost the performance and energy efficiency for real-time, agentic workloads.

Under the partnership, Gimlet plans to integrate d-Matrix Corsair accelerators into Gimlet Cloud alongside [traditional GPUs](https://www.datacenterknowledge.com/servers/understanding-gpu-servers-and-their-role-in-data-centers). In the hybrid architecture, GPUs handle compute-heavy stages of inference, while memory- and latency-sensitive operations are routed to Corsair. The companies say this split can deliver up to 10x improvements in latency and throughput per watt compared with GPU-only deployments.

“If you can fundamentally change how people interact with AI, they’ll be much more engaged,” said Zain Asgar, co-founder and CEO of Gimlet Labs, during a press briefing. “We want to enable real-time interaction with AI systems, and that starts with designing hardware and software for the workloads that matter most.”

Related:[Inference Becomes the Next AI Chip Battleground](https://www.datacenterknowledge.com/data-center-chips/inference-becomes-the-next-ai-chip-battleground)

## Heterogeneous Infrastructure for Real-Time AI

The partnership reflects a growing trend toward multi-silicon AI infrastructure – combining GPUs with inference accelerators and other specialized chips to optimize performance and efficiency.

“Inference is never a one-size-fits-all problem. Heterogeneity is the path forward,” said d-Matrix CEO Sid Sheth. “From day one, d-Matrix has focused on inference, and with power limits capping how fast AI can advance, it’s imperative that AI service providers have the right tools for the right job.”

“GPUs are great for compute-bound workloads, but memory-bound stages like token generation need something different,” added Sri Ganesan, vice president of product at d-Matrix. “Corsair breaks through the memory wall to deliver very low latency, while GPUs continue to handle the heavy compute tasks. It’s a side-by-side, better-together story.”

Analyst Matt Kimball of Moor Insights & Strategy told Data Center Knowledge that the combination of specialized hardware and software orchestration is key. “The d-Matrix architecture is designed around inference efficiency rather than training scale, which aligns with the market as AI applications move into production," Kimball said. "But hardware alone isn’t enough – platforms like Gimlet are trying to simplify deployment and integrate cleanly into existing workflows. That’s what makes this compelling.”

Kimball added that the real value is the abstraction layer Gimlet provides, which allows workloads to run across heterogeneous chips without rewriting code. “AI workloads are becoming increasingly heterogeneous, but most infrastructure is still optimized around a single accelerator type,” Kimball said. “If Gimlet can make it easy for developers to deploy across multiple chips, that could unlock real efficiency gains – a win for any enterprise. Platforms poised for success are those that developers can use without thinking about the hardware.”

Related:[How Grid-Safe Data Centers Can Help Ease the Power Crisis](https://www.datacenterknowledge.com/energy-power-supply/how-grid-safe-data-centers-can-help-ease-the-power-crisis)

## Designed for Memory-Bound AI Tasks

Corsair’s architecture emphasizes high memory bandwidth and low latency, making it well-suited for token generation and other memory-intensive operations common in [agentic AI](https://www.datacenterknowledge.com/ai-data-centers/agentic-ai-is-here-what-does-it-mean-for-data-centers-) and real-time applications.

Unlike many high-end accelerators, Corsair ships as a standard PCIe card with air cooling, enabling deployment inside existing GPU servers without specialized chassis or unconventional plumbing. d-Matrix's Jetstream NICs move data between Corsair and GPUs over standard Ethernet, simplifying integration at scale and improving utilization.

“This is about deploying at scale with minimal intervention into existing infrastructure,” Ganesan said. “The goal is to maximize hardware utilization and get the highest possible performance while staying within power constraints.”

## Targeting the Next Phase of AI Inference

The joint solution is slated to be available to select customers through Gimlet Cloud in the second half of 2026.

Related:[Retrofits vs. Rebuilds: Approaches to Adapting Legacy Data Centers for AI](https://www.datacenterknowledge.com/build-design/retrofits-vs-rebuilds-approaches-to-adapting-legacy-data-centers-for-ai)

Sheth said that initial targets include frontier AI labs building high-capability models. “Use cases include interactive coding and autonomous agents, where speed matters – both for human users and for machine-driven workflows,” Sheth said.

As agentic AI systems scale, operators are increasingly looking to specialized inference silicon to stretch limited data center power and maximize compute efficiency. “Power constraints are real," Ganesan said. “Optimizing efficiency without giving up performance is critical for cloud and enterprise deployment of AI at scale.”

* * *

## DCK Analysis: Why a Hybrid Chip Approach Matters

The d-Matrix and Gimlet partnership illustrates a shift in AI infrastructure strategy. As AI models become more complex – incorporating multi-step agents, multimodal inputs, and real-time interactions – homogeneous GPU fleets face diminishing returns on efficiency.

By pairing GPUs with memory-optimized inference accelerators and a dynamic orchestration layer, Gimlet can achieve higher throughput and lower latency without increasing total power consumption. Analyst Matt Kimball emphasized that adoption will hinge not just on hardware, but also on the software stack and the abstractions that make heterogeneity invisible to developers.

For operators, this approach offers a path to higher tokens-per-second performance per watt, better end-user interactivity, and the ability to scale frontier AI workloads within existing power budgets.

The announcement also underscores a broader trend in AI infrastructure: heterogeneous systems will likely dominate the next phase of AI deployment, with success depending as much on software orchestration as on raw hardware performance.

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## 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/).

[See more from Shane Snider](https://www.datacenterknowledge.com/author/shane-snider)

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