Next-Generation AI Computing Platform Design: “Vera Rubin”
This document outlines the design of the next-generation AI computing platform based on the real “Vera Rubin” platform released at GTC 2026. It aims to build an efficient, low-latency AI token production system by integrating cutting-edge technologies.
Core Design Principles
Heterogeneous Computing (Specialized Roles)
Rubin GPU: Responsible for the training and prefilling stages, leveraging its high throughput and large-capacity HBM4.
Vera CPU: A new component based on the Olympus architecture, featuring 88 cores, designed specifically for control and scheduling in agent AI workflows.
Groq LPU: Responsible for the decoding/inference stages, utilizing the high bandwidth advantages of SRAM to achieve ultra-low latency.
Photoelectric Interconnection (Distance-Dependent Media)
Intra-chip / Intra-rack: Utilizes NVLink 6 (3.6 TB/s, wireless backplane).
Inter-rack: Uses ConnectX-9 + CPO (1.6 Tb/s, 1550nm long-distance).
Cross-site: Optimized via optical relay stations and distance-aware scheduling.
Component Separation (Heat Source Decoupling)
Chip-level: Chiplet design separating SRAM and logic units.
Package-level: ELS (External Laser Engine), separating the optical engine from the ASIC.
Rack-level: Liquid cooling / air partitioning, with independent cooling for high-heat components.
Thermal Awareness (Cooling-Triggered Scheduling)
Physical Layer: 45°C warm water cooling + micro-channel liquid nitrogen (for high-heat zones).
Architecture Layer: The compiler detects heat distribution and actively migrates tasks to “cold zones.”
Recovery Layer: Waste heat recovery used for building heating, pushing PUE towards 1.0.
Software Abstraction (CUDA-like Programming)
Upper Layer: Keeps PyTorch/ONNX unchanged.
Middle Layer: MLIR compiler automatically maps tasks to GPUs, LPUs, and optical units.
Lower Layer: Unified ISA + Hardware Abstraction Layer (HAL), hiding heterogeneous details.
Data Flow Analysis and Key Performance Indicators
Token Generation Process Optimization
Prefill Stage (Compute-intensive): Processed by Rubin GPU.
Decoding Stage (Memory-access intensive): Processed by Groq LPU, utilizing SRAM’s high bandwidth.
KV Cache Propagation: Transmitted via CPO optical interconnects, combined with distance-aware scheduling to avoid long-distance bottlenecks.
Key Performance Indicators (KPIs)
Bandwidth: Single GPU bandwidth reaches 3.6 TB/s (NVLink 6), LPU bandwidth reaches 150 TB/s (SRAM).
Latency: Inference latency < 50 microseconds (single image, LPU deterministic scheduling).
Thermal Density: Supports handling thermal density of 5 W/mm² (micro-channel + component separation).
Energy Efficiency: PUE < 1.05 (45°C warm water + waste heat recovery).
Deployment Time: 2 hours per rack (wireless modular design).
Key Technology Integration and Roadmap
Key Enabling Technologies
Packaging & Interconnect: CPO Co-packaged Optics (early mass production), NVLink 6 / ConnectX-9 (2025-2026).
Compute Units: Groq LPU (shipping), Rubin GPU (HBM4, 2026).
Cooling Tech: 45°C water cooling (mass production), Micro-channel Liquid Nitrogen Rogen (lab stage).
Security Tech: PQC Post-Quantum Cryptography (CNSA 2.0 deployment in progress).
Processing Tech: Focused Ion Beam (FIB, mass production / small-batch research).
Implementation Roadmap
Short-term (1-2 years): Deploy Rubin GPU + Groq LPU heterogeneous clusters, build NVLink 6 + ConnectX-9 networks, deploy 45°C warm water cooling systems, and develop MLIR compilers.
Mid-term (3-4 years): Integrate CPO technology, connect optical computing units, apply micro-channel radiator particle beam processing, and fully deploy PQC.
Long-term (5+ years): Integrate optical quantum communication, achieve fully optical computing architecture, and build AI-driven adaptive thermal management systems.
Economic and Risk Assessment
Economic Analysis
Cost Structure: Hardware 60% (Compute Units 35%, Interconnect 15%, Cooling 10%), Software 25%, Operations 15%.
Expected Return: 10x increase in token generation efficiency, 50% reduction in energy costs, 75% reduction in deployment cycle, with a payback period of approximately 3-4 years.
Risk Assessment and Mitigation
Technical Risks: Optical computing devices are still in the lab stage (Mitigation: Phased integration / simulation mode); CPO mass production stability is questionable in early stages (Mitigation: Design redundancy / phased deployment).
Market Risks: Technology substitution risk (Mitigation: Maintain open architecture / retain interfaces); Requirement change risk (Mitigation: Strengthen software abstraction layer).
Security Risks: Quantum computing threats (Mitigation: Full deployment of PQC post-quantum algorithms).
This document outlines the design of the next-generation AI computing platform based on the real “Vera Rubin” platform released at GTC 2026. It aims to build an efficient, low-latency AI token production system by integrating cutting-edge technologies.
Core Design Principles
Heterogeneous Computing (Specialized Roles)
Rubin GPU: Responsible for the training and prefilling stages, leveraging its high throughput and large-capacity HBM4.
Vera CPU: A new component based on the Olympus architecture, featuring 88 cores, designed specifically for control and scheduling in agent AI workflows.
Groq LPU: Responsible for the decoding/inference stages, utilizing the high bandwidth advantages of SRAM to achieve ultra-low latency.
Photoelectric Interconnection (Distance-Dependent Media)
Intra-chip / Intra-rack: Utilizes NVLink 6 (3.6 TB/s, wireless backplane).
Inter-rack: Uses ConnectX-9 + CPO (1.6 Tb/s, 1550nm long-distance).
Cross-site: Optimized via optical relay stations and distance-aware scheduling.
Component Separation (Heat Source Decoupling)
Chip-level: Chiplet design separating SRAM and logic units.
Package-level: ELS (External Laser Engine), separating the optical engine from the ASIC.
Rack-level: Liquid cooling / air partitioning, with independent cooling for high-heat components.
Thermal Awareness (Cooling-Triggered Scheduling)
Physical Layer: 45°C warm water cooling + micro-channel liquid nitrogen (for high-heat zones).
Architecture Layer: The compiler detects heat distribution and actively migrates tasks to “cold zones.”
Recovery Layer: Waste heat recovery used for building heating, pushing PUE towards 1.0.
Software Abstraction (CUDA-like Programming)
Upper Layer: Keeps PyTorch/ONNX unchanged.
Middle Layer: MLIR compiler automatically maps tasks to GPUs, LPUs, and optical units.
Lower Layer: Unified ISA + Hardware Abstraction Layer (HAL), hiding heterogeneous details.
Data Flow Analysis and Key Performance Indicators
Token Generation Process Optimization
Prefill Stage (Compute-intensive): Processed by Rubin GPU.
Decoding Stage (Memory-access intensive): Processed by Groq LPU, utilizing SRAM’s high bandwidth.
KV Cache Propagation: Transmitted via CPO optical interconnects, combined with distance-aware scheduling to avoid long-distance bottlenecks.
Key Performance Indicators (KPIs)
Bandwidth: Single GPU bandwidth reaches 3.6 TB/s (NVLink 6), LPU bandwidth reaches 150 TB/s (SRAM).
Latency: Inference latency < 50 microseconds (single image, LPU deterministic scheduling).
Thermal Density: Supports handling thermal density of 5 W/mm² (micro-channel + component separation).
Energy Efficiency: PUE < 1.05 (45°C warm water + waste heat recovery).
Deployment Time: 2 hours per rack (wireless modular design).
Key Technology Integration and Roadmap
Key Enabling Technologies
Packaging & Interconnect: CPO Co-packaged Optics (early mass production), NVLink 6 / ConnectX-9 (2025-2026).
Compute Units: Groq LPU (shipping), Rubin GPU (HBM4, 2026).
Cooling Tech: 45°C water cooling (mass production), Micro-channel Liquid Nitrogen Rogen (lab stage).
Security Tech: PQC Post-Quantum Cryptography (CNSA 2.0 deployment in progress).
Processing Tech: Focused Ion Beam (FIB, mass production / small-batch research).
Implementation Roadmap
Short-term (1-2 years): Deploy Rubin GPU + Groq LPU heterogeneous clusters, build NVLink 6 + ConnectX-9 networks, deploy 45°C warm water cooling systems, and develop MLIR compilers.
Mid-term (3-4 years): Integrate CPO technology, connect optical computing units, apply micro-channel radiator particle beam processing, and fully deploy PQC.
Long-term (5+ years): Integrate optical quantum communication, achieve fully optical computing architecture, and build AI-driven adaptive thermal management systems.
Economic and Risk Assessment
Economic Analysis
Cost Structure: Hardware 60% (Compute Units 35%, Interconnect 15%, Cooling 10%), Software 25%, Operations 15%.
Expected Return: 10x increase in token generation efficiency, 50% reduction in energy costs, 75% reduction in deployment cycle, with a payback period of approximately 3-4 years.
Risk Assessment and Mitigation
Technical Risks: Optical computing devices are still in the lab stage (Mitigation: Phased integration / simulation mode); CPO mass production stability is questionable in early stages (Mitigation: Design redundancy / phased deployment).
Market Risks: Technology substitution risk (Mitigation: Maintain open architecture / retain interfaces); Requirement change risk (Mitigation: Strengthen software abstraction layer).
Security Risks: Quantum computing threats (Mitigation: Full deployment of PQC post-quantum algorithms).