How does NVIDIA A2 perform on Stable Diffusion?

💡 Answer

How fast is NVIDIA A2? The raw numbers: 18 TFLOPS FP16, 4.5 TFLOPS FP32, 200 GB/s memory bandwidth. In mixed-precision AI jobs, that translates to sustained throughput comfortably above older generations.

For model training, expect wall-clock times that scale predictably from those TFLOPS figures at large batch sizes. For low-latency inference, real-world latency is dominated by memory bandwidth and by how much of your KV-cache fits on-chip — so the 200 GB/s and 16 GB capacity matter more than headline TFLOPS.

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Cherry Servers GPU Provider Review & Key Facts (April 2026)

Snapshot of Cherry Servers: GPU models, pricing, billing granularity, infrastructure, developer tools, support channels, and compliance. Data verified April 2026.

Cherry Servers GPU Provider Review & Key Facts (April 2026)
Cherry Servers
Bare metal GPU servers with 24 years of hosting experience and full hardware-level control.
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Overview
Trustpilot Rating 4.6
Headquarters Lithuania
Provider Type N/A
Best For AI training inference fine-tuning rendering research HPC generative AI deep learning
GPU Hardware
GPU Models A100 A40 A16 A10 A2 Tesla P4
Max VRAM (GB) 80
Max GPUs/Instance 2
Interconnect PCIe
Pricing
Starting Price ($/hr) $0.16/hr
Billing Granularity Per-hour
Spot/Preemptible No
Reserved Discounts N/A
Free Credits None
Egress Fees N/A
Storage NVMe SSD, Elastic Block Storage ($0.071/GB/mo)
Infrastructure
Regions Lithuania, Netherlands, Germany, Sweden, US, Singapore (6 locations)
Uptime SLA 99.97%
Developer Experience
Frameworks PyTorch TensorFlow CUDA (bare metal — full stack control)
Docker Support Yes
SSH Access Yes
Jupyter Notebooks No
API / CLI Yes
Setup Time Minutes
Kubernetes Support Yes
Business Terms
Min Commitment None
Compliance ISO 27001 ISO 20000-1 GDPR PCI DSS
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