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MAMeet Amazon EC2: your virtual server in the AWS cloud. It's the core of scalable computing. Understand what an EC2 instance truly is. #AWS #EC2 #CloudComputing #VirtualServer #TechExplained #AWSCloud #ITInfrastructure
@mackdaf1rst
#Amdepyc
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112
BUAMD's MI300 chip technically competes with Nvidia's H100 on specifications. Similar performance. Lower cost per unit. Better value proposition on paper. Yet AMD captured only five percent AI market share despite being cheaper alternative.
The reason is ecosystem lock-in at catastrophic scale. Nvidia launched CUDA in 2006. Twenty years of continuous development created complete software stack for AI workloads. Every major AI framework built specifically for Nvidia architecture. PyTorch optimized for CUDA. TensorFlow optimized for CUDA. Every library, every optimization, every tool assumes Nvidia GPUs.
AI companies cannot switch without rebuilding everything from foundation. OpenAI uses hundreds of thousands of Nvidia GPUs running production inference and training workloads. Every line of training code assumes CUDA primitives. Every optimization leverages CUDA-specific features. Production systems designed around Nvidia memory architecture and interconnect topology.
Switching to AMD requires rewriting entire codebase. Millions of lines of production code must be modified. Every optimization must be reimplemented for AMD architecture. Engineers must retrain on completely different toolchain. Infrastructure teams must rebuild deployment systems. DevOps must recreate monitoring and debugging workflows.
Full episode explains how Nvidia built unbreakable moat, why technical superiority alone cannot break platform lock-in, and whether custom chips from Microsoft or Google eventually crack monopoly.
#Nvidia #AMD #CUDA #AIHardware #Monopoly #SwitchingCosts #EcosystemLockIn #OpenAI #TechBusiness #MI300 #H100 #GPUs #PlatformEconomics #AIInfrastructure #SoftwareMonopoly #VendorLockIn #TechStrategy #CompetitiveAdvantage #Moat #NetworkEffects #AICompute #MachineLearning #DeepLearning #TechIndustry #BusinessModel #StrategicAdvantage #MarketDominance #BusinessDecoded #TechMonopoly #AIRevolution
@businessdecodedteam
507
AADay 14/50
@aaron.finlogz
402
NGStop paying the “Legacy Tax” on AWS! 💸
Moving from gp2 to gp3 isn’t just a cost-saving move; it’s a total performance overhaul.
📉 Cost: ~20% lower cost per GB.
⚡ Latency: Higher baseline performance (3,000 IOPS) even on the smallest volumes.
🔄 Transition: Zero downtime, zero reboots.
In the world of AWS FinOps, this is the ultimate “low-hanging fruit.” If you haven’t switched, you’re essentially leaving money on the table and leaving performance in the tank.
Have you migrated your volumes yet? Let’s discuss in the comments! 👇
#AWS #FinOps #CloudOptimization #DevOpsEngineer #AWSCloud CloudCost TechTips SmallFixBigImpact CloudArchitecture
@ngoyal16
38.1K
AM@IBM trusts AMD as a collaborator in reimagining AI infrastructure, merging high performance compute with the agility needed for fast moving AI workloads.
See how the most trusted companies trust AMD at the link in our bio.
#TogetherWeAdvance
@amd
143
MADiscovering the 'aha' moment: connecting databases to EC2 compute. It's simpler than you think! #CloudComputing #AWS #DevOps #TechTips #DatabaseManagement #EC2 #Serverless
@mackdaf1rst
979
IAShould You Learn AWS, Azure, or GCP?
@iamsoleyman
109
CSSimplify your workflow with AWS Lambda #aws #techtips #cloudcomputing #devops
@csbrain
34.6K
AMStrong networks elevate every customer experience.
@Cisco chooses AMD to deliver the resilient, secure, high-performance compute that helps Cisco keep global enterprises online and operating without interruption.
See how the most trusted companies trust AMD at the link in our bio.
#TogetherWeAdvance
@amd
132
RDAWS EC2 Instance Launch | Step by Step Tutorial for Beginners#aws #aws #cloudcomputing #ec2 #cloudengineer
@rd_aws_solution
22
__AWS and Cerebras just teamed up to launch what they call the fastest AI inference solution on Amazon Bedrock. The partnership combines AWS Trainium processors with Cerebras' CS-3 chips, which pack 900,000 AI cores onto a single dinner-plate-sized wafer. This hybrid approach tackles the growing problem of inference costs, which often exceed training budgets for companies deploying AI at scale.
The integration puts Cerebras' CS-3 processors directly inside AWS data centers, accessible through Bedrock's unified API. AWS Trainium handles high-throughput batch processing while Cerebras delivers sub-millisecond latency for specific workloads. This dual approach routes different inference patterns to the most efficient hardware, potentially outperforming one-size-fits-all GPU solutions.
The partnership aims to undercut Nvidia H100-based solutions on speed and price. AWS promises faster, cheaper AI deployment, but has not released pricing or independent benchmarks yet. Without concrete data, many customers may stick with proven GPU deployments despite potential savings.
This signals AWS's strategic shift toward a multi-vendor silicon portfolio. Instead of relying solely on homegrown chips or Nvidia, AWS now offers differentiated performance while reducing dependence on its primary rival. Bedrock integration means customers can switch hardware backends without rewriting code.
The real test will be adoption. If benchmarks hold up, enterprises gain real choice in optimizing inference spending. If not, this becomes another interesting experiment in the race against Nvidia.
Follow us for the latest updates in tech and AI.
#AWS, #Cerebras, #AIInference
@__synapse.daily__
286
RUSee you at NVIDIA GTC 2026
@amazonwebservices x @nvidia x @rudy_chetty
@rudy_chetty
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