AWS unveils Graviton4, with 50% more cores, 75% more memory bandwidth, and up to 30% more performance than Graviton3, and Trainium2 for up to 4x faster training
Graviton4 extends that CPU roadmap while Trainium2 adds a purpose-built training option, making this a paired update to AWS's compute portfolio rather than a single-chip refresh.
First-order effects
AWS customers running compatible workloads gain access to a higher-core, higher-bandwidth Graviton generation, while AI training customers get a new Trainium option positioned around faster training.
AWS can offer more distinct infrastructure choices across general-purpose CPU workloads and model training, strengthening its control over the underlying compute stack.
Second-order effects
Rival cloud platforms and incumbent chip suppliers face added pressure to match AWS on workload-specific performance and to make migration from conventional instances less costly for customers.
The paired launches encourage customers to segment workloads by processor type, increasing the value of software tooling, frameworks, and managed services that support heterogeneous fleets.
Third-order effects
If this cadence persists, cloud competition will increasingly center on vertically integrated, workload-specific silicon portfolios rather than broadly comparable commodity instances.
The trade-off is deeper platform optimization: customers may gain more tailored price-performance but must weigh that against the operational complexity and potential dependence of adopting provider-specific compute.
The trend: This is part of the shift toward heterogeneous cloud infrastructure, where custom CPUs and AI accelerators are developed as coordinated layers of a provider's platform.
At re:Invent @awscloud announces next generaton of Graviton. As I analyze what sustainable advantage Amazon has here vs. where x86 competitors are going for cloud native silicon, it is price to performance per watt. Lower energy savings being the most sustainable. [image]
The North Star for @aws when it comes to #GenAI ? Develop and deploy the technology timely but ethically and fairly. Tune into my podcast tomorrow when I will be discussing this very topic with Diya Wynn, Responsible AI lead at AWS #reinvent [image]
First 2 product/services announcements from @aselipsky's keynote at #reinvent. 1. Storage Amazon S3 Express 2. Compute - Graviton 4 chips/instances Stay tuned for detailed commentary from me on announcements later in the day. [image]
.@awscloud unveils @Arm-based #Graviton4 #Graviton4 provides up to 30% better compute performance, 50% more cores, and 75% more memory bandwidth than current generation Graviton3 https://press.aboutamazon.com/ ... #Cloud #HPC #AI [image]
Announcing #Graviton4, @awscloud next gen processor. Companies like @SAP using #Graviton with 35% power savings. It's important for #cloud providers to offer variety of processor tech to fit user needs. With widescale acceptance, AWS will continue its own chips. #Reinvent2023 [im…
This point is the same for all hyperscalers making custom silicon. The bet here, is at some point, developers won't pick specific silicon instances but rather choose the variable (price, performance, etc) and the services layer puts your workload on the right piece of silicon.
AWS has a new Trainium2 chip for training AI models, Adam says. meanwhile, other cloud providers are ‘just talking’ about having their own AI chips, he adds https://www.cnbc.com/...