What is the average bandwidth required for GPU workloads

Direct Answer: There is no single "average" bandwidth for GPU workloads, as requirements vary by type—memory bandwidth (e.g., 1-3 TB/s for modern GPUs like H100), intra-node interconnect (e.g., 900 GB/s NVLink), or inter-node network (100-400 Gbps for distributed AI training). For cloud GPU clusters like those offered by Cyfuture Cloud, typical network bandwidth starts at 100 Gbps for basic multi-GPU setups and scales to 400+ Gbps with Infini Band for large-scale AI/HPC.​

Understanding Bandwidth in GPU Contexts

Bandwidth refers to data transfer rates critical for GPU performance, encompassing memory (HBM/GDDR), interconnects (NVLink/PCIe), and networks (Ethernet/InfiniBand). Modern GPUs like NVIDIA H100 in Cyfuture Cloud's GPU-as-a-Service achieve memory bandwidth up to 3.35 TB/s, enabling rapid data access for AI training. Insufficient bandwidth causes bottlenecks, idling GPUs during data movement in parallel workloads.​

Cyfuture Cloud optimizes this with NVIDIA A100, H100, and others, supporting scalable clusters where high bandwidth ensures efficient model training and inference. For single-GPU tasks, PCIe Gen5 (128 GB/s bidirectional) suffices, but distributed setups demand more.​

Bandwidth Types for GPU Workloads

Memory Bandwidth: Measures GPU internal data throughput. A100 offers 1.55-2 TB/s; H100 up to 3.35 TB/s; Blackwell 8 TB/s. Essential for compute-bound tasks like deep learning.​

Intra-Node Interconnect: NVLink provides 900 GB/s bidirectional per H100 GPU (18 ports), far exceeding PCIe, for multi-GPU servers.​

Inter-Node Network Bandwidth: Critical for clusters. Cloud providers like Google offer 100-3600 Gbps; AI workloads need 200-800 Gbps Infini Band per node to minimize latency in training large models.​

Cyfuture Cloud's GPU cloud infrastructure leverages these for AI/ML, HPC, delivering up to 1,555 GB/s GPU bandwidth vs. CPU's 50 GB/s.​

Factors Influencing Bandwidth Needs

Workload scale dictates requirements: single-GPU inference needs ~10-50 Gbps network; multi-node training (e.g., LLMs) requires 400 Gbps+ to achieve 90-95% scaling efficiency. AI techniques like neural networks demand high inter-GPU fabric for data parallelism.​

Provider configs matter—Cyfuture's on-demand NVIDIA GPUs integrate high-bandwidth networks, reducing costs by 50-60% via pay-as-you-go. Latency (1-5 µs InfiniBand) and RDMA further optimize.​

Cyfuture Cloud's GPU Bandwidth Solutions

Cyfuture Cloud provides GPU-as-a-Service with NVIDIA H100, A100, etc., in scalable clusters optimized for high-bandwidth AI workloads. Their architecture supports instant provisioning, high-performance interconnects like NVLink/InfiniBand equivalents, ensuring no bottlenecks for deep learning or simulations.​

Users benefit from flexible scaling (1 to hundreds of GPUs) and cost savings, with bandwidth tailored to petabyte datasets. Compared to Ethernet (100 Gbps bottlenecks), their setup enables near-linear multi-GPU scaling.​

Conclusion

GPU workloads demand bandwidth scaling from hundreds of GB/s (memory/interconnect) to 100-800+ Gbps (networks), with no fixed average—assess per use case for optimal performance. Cyfuture Cloud excels here, offering robust, cost-effective GPU infrastructure that matches enterprise AI needs without upfront hardware costs. Proper bandwidth prevents idle time, maximizing ROI in cloud environments.​

Follow-Up Questions

Q1: How does Cyfuture Cloud ensure high bandwidth for distributed training?
A: Through high-performance GPU clusters with NVLink and advanced fabrics like InfiniBand, providing 400+ Gbps inter-node bandwidth for efficient scaling.​

Q2: What bandwidth for basic AI inference on Cyfuture GPUs?
A: 50-100 Gbps network suffices, paired with 1-2 TB/s memory on T4/L40s, ideal for low-latency tasks.​

Q3: Why NVLink over PCIe in Cyfuture's offerings?
A: NVLink delivers 900 GB/s vs. PCIe 128 GB/s, reducing latency 7x for multi-GPU AI/HPC.​

Q4: Bandwidth costs in Cyfuture GPU cloud?
A: Pay-per-use model cuts costs 50-60%, with bandwidth included in scalable instances—no separate fees.​