GB300 represents not only a leap in chip-level performance, but also a milestone marking the entry of artificial intelligence infrastructure into an era of system-level innovation. From liquid cooling and power modulation to optical communication networks, each technological breakthrough is reshaping the industry landscape. This article focuses on the technical details and application value of the GB300 liquid cooling system.
1. Core Architecture of GB300 Liquid Cooling
1.1 Modular Socket Design — A Breakthrough Innovation:
GB300 adopts a socket-based processor module design (similar to a CPU socket), integrating the CPU, GPU, and HBM3 memory into a single removable module. The cold plate is embedded into the socket base, and the micro-gap between the chips and the cold plate is filled with liquid metal (thermal conductivity: 73 W/m·K) to achieve highly efficient heat dissipation.
1.2 Fully Enclosed Liquid Cooling Loop
Cooling path:
Chip heat → Liquid metal → Microchannel cold plate → Deionized water (50–60 °C) → Cooling Distribution Unit (CDU) → Outdoor dry cooler
2. Key Technological Breakthroughs
2.1 Liquid Metal Interface (LMI) Technology
Material properties:
A gallium-based alloy (mercury-free) with a melting point of 15.5 °C, excellent fluidity, and a gap-filling capability 10× greater than thermal grease.
Thermal conductivity reaches 73 W/m·K (compared to 5–12 W/m·K for conventional thermal paste), with thermal resistance as low as 0.02 cm²·K/W.
Leak prevention design:
Dual sealing rings made of fluororubber combined with electromagnetic locking prevent oxidation and leakage. The cold plate surface is nickel-plated to prevent corrosion and gallium-induced erosion of copper or aluminum substrates.
2.2 Microchannel Cold Plate Design
Structural innovation:
Microchannels with widths of less than 0.3 mm are embedded inside the cold plate, with water flow velocities of 2–4 m/s to enhance turbulent heat transfer. The cold plate is manufactured using 3D-printed titanium alloy, offering a pressure resistance of ≥10 bar, suitable for high-flow operation.
3. System-Level Integration and Efficiency Optimization
3.1 Power and Cooling Co-Design
Case study:
Google data centers utilize the heat output of GB300 to supply hot water for campus heating, achieving a PUE as low as 1.05.
Google adopts a split-flow cold plate design, which has been shown to outperform traditional straight-through cooling solutions. To further optimize heat dissipation, Google adopted a bare-die design for TPUv4, whereas TPUv3 used a lidded design. This approach is similar to the “delidding” practices seen among PC enthusiasts, where the heat spreader is removed to achieve higher thermal efficiency.
For Google, TPUv4 requires this cooling approach because its power consumption is 1.6× higher than that of TPUv3.
3.2 Deep Coupling with the Blackwell Architecture
GPU cores use TSMC’s CoWoS-L packaging, connecting the CPU, GPU, and HBM through a silicon interposer to shorten the thermal path.
Voltage Regulation Modules (VRMs) are directly embedded beneath the cold plate, eliminating the need for separate heat sinks.
4. Cost Analysis
The following summarizes the cost structure of a single GB300 rack:
Note:
Although GB300 racks are approximately 35% more expensive than air-cooled racks, their total cost of ownership (TCO) is reduced by 20%.
5. Challenges
Despite significant breakthroughs in cooling efficiency, large-scale deployment of GB300 liquid cooling technology still faces two major challenges:
Liquid metal management:
Long-term operation may lead to thermal degradation due to oxidation, requiring maintenance approximately every two years.
Infrastructure dependency:
The system requires 800 V high-voltage DC power and dedicated liquid cooling pipelines, posing significant challenges for retrofitting legacy data centers.
6. NVIDIA GB300 Industry Case Studies
Gigawatt-Scale AI Factories: NVIDIA GB300 NVL72 Based on Lambda Cloud
Recently, the first batch of NVIDIA GB300 NVL72 systems has been officially deployed in Lambda’s high-density liquid-cooled data centers.
Each rack integrates:
72 NVIDIA Blackwell Ultra GPUs
36 NVIDIA Grace CPUs
37 TB of high-speed memory
130 TB/s NVIDIA NVLink switch bandwidth
These capabilities significantly enhance performance for frontier research and enterprise AI deployments.
Architectural Improvements over NVIDIA GB200 NVL72
Compared to NVIDIA GB200 NVL72, the NVIDIA GB300 NVL72 delivers substantial architectural enhancements:
HBM3e capacity increased by 50% (20 TB per rack), supporting trillion-parameter models with larger checkpoints, higher batch sizes, and expanded context windows.
Dense FP4 performance improved by 1.5×, with attention operation speed doubled, improving inference efficiency and utilization for inference-intensive workloads.
Designed for Large-Scale Inference
NVIDIA GB300 NVL72 is purpose-built for large-scale inference workloads, which may require up to 100× more computation per query compared to single inference tasks.
The expanded HBM3e memory and highly efficient FP4 performance ensure full-speed operation.
Rack-scale NVLink 5 provides 1.8 TB/s bandwidth per GPU, interconnected via NVLink Switch into a unified high-speed fabric to enable model parallelism.
For multi-rack clusters, NVIDIA Quantum-X800 InfiniBand and ConnectX-8 SuperNIC deliver 800 Gb/s per GPU, reducing communication overhead during distributed training and inference.
Direct-to-chip liquid cooling ensures these systems operate at peak utilization without thermal throttling, enabling the deployment densities required for gigawatt-scale AI factories.
Platform Positioning and Software Integration
NVIDIA GB300 NVL72 is designed for superintelligence and enterprise AI, featuring a second-generation Transformer Engine with dynamic range management and fine-grained scaling, significantly improving inference efficiency.
Compared with NVIDIA GB200 NVL72:
Memory capacity increases by 1.5×
FP4 performance improves by up to 50%
Lambda’s deployed NVIDIA GB300 NVL72 clusters integrate compute, storage, orchestration, and observability into a single system. Each rack provides 3.84 TB of NVMe cache per GPU (276 TB per NVL72), configurable parallel file storage for high-throughput data access, optional Kubernetes or Slurm managed orchestration, and unified observability via Prometheus and Grafana for real-time metrics and alerts.
Conclusion
By integrating modular socket-based design, liquid metal interfaces, and high-voltage liquid cooling loops, NVIDIA GB300 elevates liquid cooling from a peripheral system to a chip-level core solution. Its value lies not only in supporting ultra-high thermal loads of up to 1400 W, but also in redefining server architecture paradigms.
NVIDIA positions the GB300 NVL72 as a flagship configuration: liquid-cooled racks equipped with Grace Blackwell Ultra superchips, delivering exa-scale FP4 performance density and significantly higher throughput per megawatt compared to previous HGX platforms. This is expected to accelerate the evolution of data centers toward “zero-emission heat recovery.”
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Written by
CoolingThermal Engineering TeamCoolingThermal is an automation equipment manufacturer based in Kunshan, China, specializing in heat pipe and vapor chamber production equipment since 2017. Our engineering team designs, builds, and commissions complete production lines covering forming, degassing, welding, testing, and assembly processes. The technical content on this blog is written by the same team that develops the equipment — based on real production experience, not secondary research.
