DriveNets

DriveNets is a leader in high-scale networking software for AI infrastructure and service providers.

[DriveNets @drivenets]

Watch now!

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝗳𝗮𝗯𝗿𝗶𝗰 𝗳𝗼𝗿 𝗔𝗜
🎦 https://t.co/Oo0c1OwlrD

@DriveNets and @EdgecoreNetwork explore how to build a lossless, congestion-free AI fabric using Ethernet — delivering InfiniBand-class performance at a fraction of the cost and driving the utilization of your GPUs up and to the right...

What you'll learn!

How Fabric Scheduled Ethernet (FSE) and the DriveNets OS on Edgecore platforms enables:
▶️ Predictable, lossless performance for AI clusters
▶️ High-scale fabrics built on standard Ethernet
▶️ Support for any GPU or NIC
▶️ Major reductions in AI infrastructure and operational costs

Trim millions of dollars off the costs associated with building and maintaining a large AI cluster, with any GPU or NIC.

[DriveNets @drivenets]

As the overall AI networking TAM climbs above $200B, operators are moving toward heterogeneous, multivendor AI fabrics.

👉 https://t.co/e3L8X8Bs1B

Alan Weckel, founder and analyst at @650Group shows how DriveNets’ $410M funding round will strengthen its position in AI networking by increasing inventory investment for supply-constrained markets and expanding its heterogeneous model across more AI accelerator vendors.

That can help operators improve cost models and accelerate deployment cycles as AI infrastructure scales.

With multivendor AI stack optimization and heterogeneous AI readiness, DriveNets is positioned to help Hyperscalers, NeoClouds, and enterprises build the next wave of AI networks.

[DriveNets @drivenets]

The most expensive idle asset in the world? A GPU waiting on the network.

When networking bottlenecks slow down your AI clusters, your ROI plummets.

DriveNets just secured $410M in Series D funding to scale the open, multi-vendor Ethernet fabrics that large-scale AI infrastructure demands.
https://t.co/O2zyQXiqBo

DriveNets’ high-performance AI Fabric eliminates networking bottlenecks by performing end-to-end networking optimizations across the entire AI stack.

✔️ Maximize GPU utilization
✔️ Reduce cost-per-token
✔️ Enable rapid deployment and efficient end-to-end scaling

[DriveNets @drivenets]

DriveNets は Interop 26 Tokyo に出展します

小間番号: 5Y15

DriveNets Network Cloud が、サービ���プロバイダーのインフラ設計に革新を起こす。AI GPU クラスターを支えるハイパースケーラーの選択肢とは？オープンでスケーラブル、かつハイパフォーマンスなネットワークの最前線を、Interop の現場でご体感ください。

Masao Inouye​ Tatasuyoshi Semboku ​ Hideya Kaneko​ Atsushi Shimazaki​ Shunsuke Sasaki​ Masaru Akai​ Hironari Harimoto

出典案内リンク: https://t.co/s8BufTcPS9

タイトル: 本番環境に対応する AI 推論インフラ設計 ― AMD Instinct GPU 活用の実践

セミナー登録: https://t.co/yBMLIpeLYw

G2-07 - 06.11(木) 15:10-15:50 展示会場内  (C732)

��要: AI 活用の拡大に伴い、推論インフラには単なるベンチマーク性能ではなく、安定性・効率性・運用性を含めた「本番品質」が求められています。特に大規模環境では、ノード単体の性能だけでなく、ネットワークやソフトウェアを含めたシステム全体の最適化が不可欠です。本セッションでは、DriveNets が AMD AI リファレンスアーキテクチャの構築を通じて得た知見をもとに、実運用に耐える AI 推論インフラ設計の考え方を解説します。ノード、ネットワーク、ソフトウェアスタックを横断したチューニングの重要性とともに、スケーラブルかつ効率的な推論基盤を実現するための具体的なアプローチを紹介します。AI インフラを検討・設計するすべての企業にとって、実践的な示唆を提供するセッションです。

Speaker: 日本ドライブネッツ（株）シニアセールスエンジニア 兼 エバンジェリスト

仙北 龍若

タイトル: レガシールーターからオープンネットワークへ ― Tier-1 事業者のための現実的アプローチ）

セミナー登録: https://t.co/P4vPeSURhk

J3-03 - 06.12(金) 11:25-12:05 展示会場内RoomG(7A09)

概要: 通信事業者は、スケーラビリティとコスト効率を両立するため、従来の専用機器中心のネットワークからの脱却を迫られています。その中で、オープン・ディスアグリゲーションは、現実的かつ実行可能なアーキテクチャとして注目されています。本セッションでは、DriveNets が、ホワイトボックスやマーチャントシリコンを活用し、クラウドライクな運用モデルを取り入れたキャリアグレードネットワークへの移行方法を解説し���す。分断された従来環境から統合されたネットワークへの進化を、コンバージェンスと���動化の観点から具体的に示します。実導入を見据えたステップと考慮点を整理し、ネットワーク変革に取り組む事業者に向けた実践的な指針を提示します。

Speaker: 日本ドライブネッツ（株）シニアセールスエンジニア, 佐々木 俊輔

[DriveNets @drivenets]

AI Networking for Service Providers: A Different Approach with DDC Clusters

Juan Rodriguez, Senior Sales Engineer at @drivenets explains how service providers are adopting AI networking through a "third" approach leveraging distributed disaggregated chassis (DDC) cluster technology with fabric architecture.
https://t.co/XL8gTJbJG6

[DriveNets @drivenets]

AI infrastructure success isn't measured by the number of GPUs you own.

Find out why in our latest blog post: https://t.co/R7MmnY8unG

The AI landscape is shifting fast. As the industry moves from heavy model training to continuous AI inference, the "one size fits all" approach to hardware is not enough.

Read the full post:

✔️ Why traditional TCO metrics are misleading (and the new metric to use)
✔️ How multi-vendor, multi-ASIC clusters are tackling the unique demands of AI inference
✔️ Why the network is the critical piece of infrastructure that makes or breaks heterogeneous AI

Get ready for the next AI infrastructure revolution.

[DriveNets @drivenets]

Scaling AMD AI clusters?​

Let's talk about optimizing them for AI performance. ​

Dive into the process and benchmark results behind a system-level optimization journey, starting deep within the host and extending across the entire multi-node network fabric.​

In our latest benchmarks using AMD Instinct MI355X GPUs, we prove what deep optimization can deliver:​

✔️ 15% faster Time to First Token (TTFT) at the single-node level​

✔️ Up to 5% higher multi-node throughput​

✔️ 12–16% faster TTFT at scale​

✔️ Stronger performance as concurrency increases

https://t.co/riE0hs8wJE

[DriveNets @drivenets]

DriveNets は Interop 26 Tokyo に出展します

小間番号: 5Y15

DriveNets Network Cloud が、サービスプロバイダーのインフラ設計に革新を起こす。AI GPU クラスターを支えるハイパースケーラーの選択肢とは？オープンでスケーラブル、かつハイパフォーマンスなネットワークの最前線を、Interop の現場でご体感ください。

出典案内リンク: https://t.co/s8BufTci2B

タイトル: 本番環境に対応する AI 推論インフラ設計 ― AMD Instinct GPU 活用の実践

セミナー登録: https://t.co/QmdgETwdB2

G2-07 - 06.12(木) 15:10-15:50 展示会場内  (C732)

概要: AI 活用の拡大に伴い、推論インフラには単なるベンチマーク性能ではなく、安定性・効率性・運用性を含めた「本番品質」が求められています。特に大規模環境では、ノード単体の性能だけでなく、ネットワークやソフトウェアを含めたシステム全体の最適化が不可欠です。��セッションでは、DriveNets が AMD AI リファレンスアーキテクチャの構築を通じて得た知見をもとに、実運用に耐える AI 推論インフラ設計の考え方を解説します。ノード、ネットワーク、ソフトウェアスタックを横断したチューニングの重要性とともに、スケーラブルかつ効率的な推論基盤を実現するための具体的なアプローチを紹介します。AI インフラを検討・設計するすべての企業にとって、実践的な示唆を提供するセッションです。

Speaker: 日本ドライブネッツ（株）シニアセールスエンジニア 兼 エバンジェリスト

仙北 龍若

タイトル: レガシールーターからオープンネットワークへ ― Tier-1 事業者のための現実的アプローチ）

セミナー登録: https://t.co/elpdGiG8YN

J3-03 - 06.12(金) 11:25-12:05 展示会場内RoomG(7A09)

概要: 通信事業者は、スケーラビリティとコスト効率を両立するため、従来の専用機器中心のネットワークからの脱却を迫られています。その中で、オープン・ディスアグリゲーションは、現実的かつ実行可能なアーキテクチャとして注目されています。本セッションでは、DriveNets が、ホワイトボックスやマーチャントシリコンを活用し、クラウドライクな運用モデルを取り入れたキャリアグレードネットワークへの移行方法を解説します。分断された従来環境から統合されたネットワークへの進化を、コンバージェンスと自動化の観点から具体的に示します。実導入を見据えたステップと考慮点を整理し、ネットワーク変革に取り組む事業者に向けた実践的な指針を提示します。

Speaker: 日本ドライブネッツ（株）シニアセールスエンジニア, 佐々木 俊輔

[DriveNets @drivenets]

We remember.

[DriveNets @drivenets]

Stop by Booth 506

DriveNets is proud to be a Bronze Sponsor at Dell Technologies World 2026! ​

Learn more about how our high-performance AI networking solution ensures optimal GPU utilization and fast deployment - addressing the needs of
✔️ large-scale multi-tenant AI clusters
✔️ clusters deployed across multiple sites
✔️ clusters supporting GPU as a Service (GPUaaS) offering
✔️ converged back-end and storage networks

#delltechworld

[DriveNets @drivenets]

Scale AI Clusters with DriveNets and @Dell

Build AI fabrics with the performance, reliability, and economics required for demanding AI workloads.
https://t.co/6719UV4SUh

DriveNets Fabric Scheduled Ethernet on the Dell AI Factory helps deliver predictable, high-performance AI networking for multi-tenant, GPUaaS, scale-across, and unified back-end/storage environments.

Build high-performance AI clusters:
- Consistent, low-latency AI performance
- Lossless Ethernet fabric for demanding AI workloads
- Faster deployment with full-stack AI cluster orchestration

Meet us at Booth 506 at #DellTechWorld to learn more about this new solution.

[DriveNets @drivenets]

Is your network the hidden ceiling for your AI performance?​

While Mixture-of-Experts (MoE) architectures have drastically reduced compute costs, they have exposed a critical networking bottleneck that GPU investment alone cannot fix. ​

Unlike the predictable, choreographed communication of dense models, MoE creates "improvisational" and unpredictable traffic patterns that often lead to significant GPU underutilization. ​

👉 https://t.co/uZvoFgEB67

Explore how industry leaders like DeepSeek AI and Meta are already reporting that communication latency can account for up to 50% of training time or 30% of serving latency.  ​

The focus must now shift from raw processing power to optimizing the network fabric that connects it all.

[DriveNets @drivenets]

Is the network the hidden bottleneck holding back AI cluster performance?

Let's meet at the @ONUG_ AI Networking Summit

📍 DriveNets stand 41
🗓 March 13-14, 2026 - Frisco|Dallas

Hear our presentation!
🎤 The Most Important Part of Your AI Cluster Isn’t What You Think

📅 May 14 | 12:30-12:40 pm CT
Sani Ronen will break down the building blocks of AI clusters and how the most critical parts can be optimized for peak performance

[DriveNets @drivenets]

Come and see DriveNets at the ONUG AI Networking Summit
📍 Stand 41
🗓 May 13-14, 2026 - Frisco|Dallas

Join our session! 🎤
The Most Important Part of Your AI Cluster Isn’t What You Think
📅 May 14 | 12:30-12:40 pm CT
Sani Ronen, DriveNets Director of AI Networking, will break down the building blocks of AI clusters and how the most critical parts can be optimized for peak performance.
👉 https://t.co/NJXPqNsMzw

[DriveNets @drivenets]

NEW Report! Top Trends in Networking for AI​

​ 👉 https://t.co/S8tXsO8ygI

@futuriom explores the growing challenges of networking AI workloads - from datacenter clusters to the edge - covering areas like power consumption, emerging standards, and vendor solutions - as agentic AI rapidly becomes central to business operations.

[DriveNets @drivenets]

Is your network the hidden ceiling for your AI performance?​

While Mixture-of-Experts (MoE) architectures have drastically reduced compute costs, they have exposed a critical networking bottleneck that GPU investment alone cannot fix. ​

Unlike the predictable, choreographed communication of dense models, MoE creates "improvisational" and unpredictable traffic patterns that often lead to significant GPU underutilization. ​

👉 https://t.co/uZvoFgE3gz

Explore how industry leaders like DeepSeek AI and Meta are already reporting that communication latency can account for up to 50% of training time or 30% of serving latency.  ​

The focus must now shift from raw processing power to optimizing the network fabric that connects it all.

[DriveNets @drivenets]

Your AMD infrastructure can perform better.

DriveNets and @AMD provide a fully integrated software and hardware stack that serves as a high-performance, competitive alternative for AI infrastructure.

Through our collaboration, we've published a validated Reference Architecture for clusters built with AMD Instinct MI355X GPUs, AMD Pollara NICs, and DriveNets scale-out and frontend solutions.

Read the full Reference Architecture and benchmark results:
👉 https://t.co/chN2YgVy7y

By combining AMD’s capabilities with the DriveNets full-system approach, a solution built on AMD Instinct can easily handle the real-world demands of running modern AI inference in production.

We prove what deep optimization can deliver:

✔️ 15% faster Time to First Token (TTFT) at the single-node level

✔️ 12–16% faster TTFT at scale

✔️ Up to 5% higher multi-node throughput

✔️ Stronger performance as concurrency increases

[DriveNets @drivenets]

Let's meet in Vegas!

DriveNets is proud to be a Bronze Sponsor at Dell Technologies World 2026! ​

Stop by Booth 506 to see how we’re helping organizations to implement an open, Ethernet-based alternative to InfiniBand that’s built for the most demanding AI workloads. ​

#DellTechWorld #DellTechnologies

[DriveNets @drivenets]

Don’t let standard software constraints hold back your compute investment. See how we are unlocking the full power of the AMD ecosystem.

Read the full technical deep dive here: https://t.co/1mK2OzK5gq

[DriveNets @drivenets]

Stop touching boxes!

Zero-touch provisioning isn’t new. But making it cluster-wide, consistent, and reliable at scale is where things get interesting.

https://t.co/DkajB9yd6T

In this new TechBites, Brad Riapolov breaks down how we:
🔹 Move from per-device bring-up to cluster-level initialization
🔹 Enforce state consistency across all nodes
🔹 Eliminate configuration drift before it even starts

The result: faster deployment, fewer human errors, and predictable outcomes across large-scale systems - not just individual boxes.

👉 Follow #DNTechBites for real-world engineering insights, protocol breakdowns, and networking architecture innovations