What are the most common GPU configurations used in cloud servers?

Cloud servers commonly feature NVIDIA GPUs like A100, H100, L40S, and T4 in configurations ranging from 1-8 GPUs per instance, paired with high-core CPUs and substantial RAM for AI, ML, and graphics workloads. These setups balance performance, cost, and scalability across providers like AWS, Google Cloud, and specialized hosts such as Cyfuture Cloud.​

Overview of GPU Configurations

Major cloud providers standardize GPU setups around NVIDIA's data center GPUs due to their CUDA ecosystem dominance. Configurations typically attach 1 to 8 GPUs to VM instances with 8-224 vCPUs and 32-2,952GB RAM. For instance, Google Cloud's A3 series pairs 8x H100 GPUs (141GB HBM3e each) with 208 vCPUs for ultra-high training throughput.​

AWS favors G4dn (up to 8x T4) and P5 (8x H100) instances, while Azure mirrors with ND-series. Entry-level options like 1x L4 (24GB GDDR6) suit inference on G2-standard VMs, scaling to 8x for enterprise needs. Cyfuture Cloud mirrors these with bare-metal GPU servers, emphasizing low-latency passthrough for AI workloads in India.​

Popular NVIDIA GPU Models in Clouds

NVIDIA holds over 90% market share in cloud GPUs. Key models include:

- H100/H200 (Hopper Architecture): 80-141GB HBM3e VRAM, ideal for LLMs. Common in 8-GPU pods (e.g., Google A3-ultragpu-8g: 8x1128GB total).​

- A100/A10 (Ampere): 40-80GB HBM2e, versatile for training/inference. Often 4-8 GPUs (e.g., AWS P4d: 8x A100).​

- L4/L40S (Ada Lovelace): 24-48GB GDDR6, efficient for inference. 1-4 GPUs standard (e.g., Google G2: up to 2x L4).​

- T4 (Turing): 16GB GDDR6, budget-friendly for VDI/ML. Up to 4x in G4 instances.​

AMD MI300X and Intel Gaudi appear less frequently, mainly in niche hyperscalers. Cyfuture integrates these NVIDIA staples with NVLink for multi-GPU scaling.​

Use Cases and Selection Factors

Configurations align with workloads:

Factors like FP16 throughput, interconnect (NVLink/InfiniBand), and pricing dictate choices—H100 excels in precision-heavy tasks but costs more than L4. Cyfuture's workload analysis ensures optimal picks for cost-performance.​

Cyfuture Cloud GPU Offerings

Cyfuture Cloud, a leading Indian provider, delivers GPU servers with NVIDIA H100, A100, and L40S in 1-8 GPU configs, featuring AMD EPYC CPUs, 1-6TB DDR5 RAM, and 10-100Gbps networking. Passthrough mode offers full isolation for deep learning, while vGPU sharing boosts density. Benefits include Delhi data centers for low latency, green cooling, and pricing 30-50% below global hyperscalers.​

Their infrastructure supports Kubernetes orchestration and auto-scaling, powering AI startups and enterprises under data localization laws.

Conclusion

The most common cloud GPU configurations revolve around NVIDIA's H100, A100, L4, and T4 in 1-8 GPU setups, tailored for AI/ML dominance. Cyfuture Cloud enhances accessibility with India-centric, high-speed options for seamless scaling. Adopting these ensures future-proof compute without upfront hardware costs.​

Follow-Up Questions

Q1: How do GPU configs differ between training and inference?
Training favors 4-8x high-VRAM GPUs like H100 for parallel processing; inference uses 1-2x efficient L4/T4 for low latency. Cyfuture tunes both via workload profiling.​

Q2: What are costs for common Cyfuture GPU servers?
1x H100 starts at ₹50,000/hour; 8x A100 clusters from ₹3 lakhs/hour—pay-as-you-go with reserved discounts up to 60%. Exact quotes via their portal.​

Q3: Are AMD/Intel GPUs viable alternatives?
Less common due to ecosystem gaps, but AMD MI300X offers HBM3e value in select clouds. Cyfuture prioritizes NVIDIA for CUDA compatibility.​

Q4: How to migrate to Cyfuture GPU cloud?
Use their Terraform APIs for lift-shift, with free migration tools and 24/7 support. Benchmarks show 20% faster inference vs. legacy setups.​