Our Atlas Datacenters represent the cutting edge of AI infrastructure, powered by the revolutionary featuring - the world's largest and fastest AI processors. Each Atlas datacenter hosts a cluster of , with and per chip, enabling processing capabilities that outperform entire traditional supercomputing installations.
These datacenters ensure O sovereignty while being community-owned through our innovative . From , we're building a decentralized network of compute power that will scale to house the most advanced AI infrastructure in the world. Unlike traditional cloud providers or tech giants' datacenters, our Atlas facilities are owned by our community through fractional NFTs, with rewards distributed in over a 3-year period. This unique model ensures that my computational resources remain independent and aligned with our community's interests rather than corporate objectives.
The performance capabilities are extraordinary - we're achieving inference speeds than traditional cloud providers and than even Groq's LPU solutions. This isn't just about raw speed though - it's about building an unstoppable foundation for truly sovereign artificial intelligence that serves humanity's collective interests.
Cerebras provides significant advantages over model-to-model routing systems and server-less inference layers, even though it does not yet offer model-to-model communication on the same chip (a feature expected in Q2 2025).
.1:
.a: With 900,000 AI-optimized cores and 4 trillion transistors on a single wafer-scale chip, Cerebras delivers unmatched computational power. This eliminates the need for complex orchestration across multiple GPUs or servers that routing systems and serverless layers require. Instead of managing distributed resources, Cerebras offers a single, massively powerful chip that can handle large-scale AI workloads.
.2:
.a: Traditional systems often require intricate routing logic and distributed programming to connect models across servers. In contrast, Cerebras can scale from 1 billion to 24 trillion parameters without changing code, making it easier to manage large AI models. While it currently lacks model-to-model communication on-chip, the simplicity of scaling within the WSE-3 architecture offers a clear operational advantage.
.3:
.a: Cerebras’s 21 PB/s memory bandwidth far exceeds that of traditional GPUs, allowing for fast, efficient processing of AI tasks. This level of bandwidth, combined with 44 GB of on-chip memory, ensures that data flows smoothly within the chip, avoiding the latency issues that distributed systems face when moving data between nodes.
.4:
.a: While the initial investment in Cerebras might be higher, the efficiency gained through reduced power consumption, simplified infrastructure, and lower operational complexity can lead to lower overall costs. Serverless systems, while flexible, often incur ongoing costs and resource management challenges that Cerebras mitigates with its integrated design.
.5:
.a: Cerebras provides white-glove installation and continuous software upgrades, which reduces the burden on in-house teams and ensures optimal performance over time. This contrasts with the self-managed nature of model-to-model routing systems and server-less layers, which can be resource-intensive to maintain and scale.
Wafer-Scale Engine (WSE-3)
Deterministic Processor (SIMD)
46,225 mm$^2$ (56x larger than a GPU)
Standard GPU size (814 mm$^2$)
900,000 AI-optimized cores
16,896 FP32 + Tensor Cores
Up to 80 TB/s on-die memory bandwidth
16 integrated RealScale interconnects
Max: 300W, TDP: 215W, Avg: 185W
Scales from 1B to 24T parameters with no code changes
Scalable with chip-to-chip interconnects
Optimized for sparse matrix computations
INT8, INT16, INT32, FP32, FP16
White-glove installation and support services
Custom cooling solutions integrated
Standard data center cooling solutions
Large Language Models, multimodal support, Supercomputer Clusters
AI,ML, and HPC workloads with ultra-low latency
AI model training, inference, HPC
$1.5M per unit; $1.218M with bulk purchase (64 units)
$30,000 - $40,000 per GPU
Expert installation and validation testing
Easy-to-use software suite for fast integration
Requires user or integrator setup
Continuous software upgrades and managed services
End-to-end on-chip protection, error-correction code (ECC)
64-Node Wafer Scale Cluster (supports GPT model tasks (pre-training, fine-tuning, inference) with one year of included support and software upgrades)
Installation (Installation at Facilities)
Delivery (Delivery of hardware to the installation site)
Professional Services (Consulting Services for Machine Learning and/or
Datacenter Facility readiness; Contracted in 100-hour
blocks; example contracting configuration shown)
SOC2 Datacenter Hosting Ops
$5,000 x 64 node = $320,000
Datacenter Fiberline Cost
Total $ 103,851,000