Send us something, Share your comments directly :)

هل شراكة AMD و Cerebras هتغير شكل داتا سنترز الذكاء الاصطناعي؟

في الحلقة دي، بنشرح الإعلان المهم اللي حصل في مؤتمر AMD Advancing AI 2026، وليه معمارية الـ Disaggregated Inference بقت أهم بكتير من فكرة إنك تزود كروت (GPUs) وخلاص.

عملية الـ Inference في الـ LLMs مش خطوة واحدة؛ دي بتتقسم لمرحلتين مختلفين تماماً عن بعض في متطلبات الهاردوير:

مرحلة الـ Prefill: ودي عملية معقدة حسابياً (Compute-bound) ومحتاجة قدرة معالجة ضخمة عشان تعالج الـ Input وتطلع أول Token.

مرحلة الـ Decode: ودي معتمدة تماماً على سرعة الذاكرة (Memory-bandwidth bound) وزمن الاستجابة عشان تبدأ تطلع الـ Tokens ورا بعض.

في الحلقة دي هنتكلم عن:

ليه الـ Prefill والـ Decode كل مرحلة فيهم محتاجة نوع هاردوير مختلف تماماً؟

شرح مبسط لمفاهيم زي الـ TTFT و ITL وإيه هو الـ KV Cache.

ليه تشغيل المرحلتين على نفس الـ GPU Pool بيعمل عنق زجاجة (Bottleneck) ويهدر موارد السيرفرات؟

إزاي بنفصل الـ Workloads باستخدام راك AMD Helios مع شريحة Cerebras WSE-3؟

تحديات الفابريك والشبكات: يعني إيه Latency Tax وقت نقل الـ KV Cache بين الكلاسترز المختلفة؟

بتشبيهات وأمثلة عملية من الواقع، هنشوف إزاي المعمارية دي شغالة، وليه مستقبل البنية التحتية للذكاء الاصطناعي مبقاش متوقف على عدد الـ GPUs، بل على أوبتيمايزيشن المسار الكامل (End-to-End) للـ Tokens.

والسؤال الأهم: هل الـ Disaggregated Architecture هتكون هي المعيار الجديد، ولا تكلفة الشبكات والـ Latency هتعطل الفكرة؟

Follow us on

Apple Podcast
Google Podcast
YouTube Channel
Spotify

Send us something, Share your comments directly :)

In this episode of TelcoBytes, we explore the AI-Ready Datacenter from a Network and Infrastructure perspective. As Artificial Intelligence continues to reshape the technological landscape, the underlying network must evolve to handle unprecedented bandwidth and latency demands.

We provide a comprehensive technical comparison of the AI networking portfolios from the top four industry leaders:

• Arista Networks: We discuss their dominance with Hyperscalers, heavy reliance on Merchant Silicon (Broadcom Tomahawk series), and innovations like the Distributed Etherlink Switch to optimize Job Completion Time (JCT).
• HPE Juniper Networks: A deep dive into their hybrid silicon strategy leveraging both Express 5 (Custom Silicon) and Tomahawk. We cover their use of High Bandwidth Memory (HBM) for mitigating incast traffic and Apstra's intent-based, multi-vendor fabric management capabilities.
• Cisco: An overview of their Full Stack approach, strategic hardware partnership with Nvidia, the Silicon One architecture, and their push for power efficiency using Linear Pluggable Optics (LPO).
• Huawei: An analysis of their vertical integration and full-stack AI ecosystem, highlighting the CloudEngine series, NSLB (Network-Scale Load Balancing) for lossless performance, and advanced liquid cooling designs.

Whether you are building a massive GPU cluster or optimizing enterprise AI workloads, understanding these network architectures is critical. Tune in to discover how to choose the right infrastructure for your AI deployments.

Follow Us:

https://www.linkedin.com/in/telco-bytes

https://www.linkedin.com/in/ledeeb

https://www.linkedin.com/in/bassem-aly

Follow us on

Apple Podcast
Google Podcast
YouTube Channel
Spotify

Send us something, Share your comments directly :)

Welcome to a new episode of TelcoBytes! Today, we are mapping out the next 12 months in the world of AI-Ready Datacenters. After spending trillions on training massive Large Language Models (LLMs), the industry is aggressively pivoting towards Inferencing in 2026.

In this discussion, we decode "Inference Economics" and explain why generating output tokens behaves so differently than traditional CPU workloads. We also explore the intense "Silicon War" where Hyperscalers like Google, Microsoft, and AWS are developing custom chips to challenge Nvidia's dominance, while AMD makes strategic plays to secure its market share.

Finally, we highlight the Golden Opportunity for Telco providers. With centralized datacenters facing physical latency limits for critical use-cases (like robotics and autonomous vehicles) and strict data sovereignty regulations, Telcos are perfectly positioned to win. By transforming Central Offices and RAN sites into distributed Micro-Datacenters for Edge AI, telecom operators can move from merely providing "dumb pipes" to delivering fully hosted, ultra-low latency AI applications.

Follow Us:

LinkedIn (TelcoBytes): https://www.linkedin.com/in/telco-bytes

LinkedIn (Mohamed Eldeeb): https://www.linkedin.com/in/ledeeb

LinkedIn (Bassem Aly): https://www.linkedin.com/in/bassem-aly

Follow us on

Apple Podcast
Google Podcast
YouTube Channel
Spotify