Rent NVIDIA A2 GPUs for 20× Faster Inference

Choose A2 when you want to accelerate lightweight to medium inference workloads, video analytics or edge ML without paying for data-center class GPUs or higher power draws. If you need heavy training, large LLMs or very high throughput across many models, GPUs like A30, L4, A100 or H100 are usually a better fit.

Servers with A2 GPUs can deliver up to roughly 20× higher inference performance than CPU-only servers for typical AI workloads, while also being more efficient for intelligent video analytics than previous GPU generations. That means you can hit latency targets with fewer servers and lower power usage.

A2 shines for:

• Real-time video analytics and multi-camera monitoring
• Computer vision models (detection, classification, tracking)
• NLP and other small to mid-size text inference workloads
• Speech and audio AI (ASR, keyword spotting, voice commands)
• Edge ML deployments in stores, factories, kiosks and IoT gateways

Anywhere you need inference, not huge training, in a tight power or space envelope, A2 is a strong candidate.

Yes. A2 includes dedicated media engines (NVENC/NVDEC) that handle hardware-accelerated encode and decode for common codecs like H.264, H.265, VP9 and AV1 (decode). This makes it ideal for video analytics, live monitoring, and low-latency streaming pipelines on the edge.

On AceCloud, NVIDIA A2 instances give you:

• 16 GB GDDR6 GPU memory
• Around 200 GB/s memory bandwidth
• PCIe Gen4 x8 interface
• 40–60 W configurable TDP in a low-profile form factor

These specs keep A2 power efficient while still fast enough for demanding inference and video workloads.

A2 is designed for lightweight inference, not large LLM training. It can run smaller or quantized models, but if you plan to train or serve big LLMs and diffusion models at scale, you should look at AceCloud’s A100, H100, H200 or L40S instances instead.

Yes. You can run common AI stacks such as PyTorch, TensorFlow, JAX, TensorRT and NVIDIA Triton Inference Server on A2. AceCloud also offers prebuilt GPU images and containers with CUDA and popular frameworks configured, or you can bring your own images and deploy them via Docker or Kubernetes.

You can scale A2 horizontally by adding more GPU nodes as traffic increases, or vertically by choosing larger instance flavors with more vCPUs and RAM. A2 is typically used as a single-GPU instance; for multi-GPU distributed training or heavy parallel workloads, AceCloud recommends A30 or A100 clusters instead.

Yes. A2 instances on AceCloud are built for continuous production workloads such as round-the-clock video analytics or always-on inference services. You can keep them running 24/7 on longer-term plans or use on-demand modes for bursty jobs, with monitoring and support from the AceCloud team.

AceCloud delivers A2 as dedicated GPU instances by default so your workloads get predictable performance and isolation. NVIDIA A2 supports vGPU software for virtual desktops and shared GPU scenarios, and AceCloud can enable virtualization features in specific deployments. If you need vGPU-based VDI or multi-tenant sharing, talk to our team for architecture options.

A2 pricing depends on the flavor you pick (GPU count, vCPU, RAM, region and term). You can see exact hourly and monthly rates on the A2 pricing page and compare against other GPU types. New customers usually qualify for free credits so they can benchmark A2 for their workloads before committing.