Rent NVIDIA A800 PCIE GPU by the hour
The NVIDIA A800 PCIE is an Ampere architecture datacenter GPU equipped with 80 GB of VRAM. It delivers about 1598 GB/s of memory bandwidth and about 15.6 TFLOPS of single-precision performance within a 300 W power envelope. On ONIE, you can deploy this card with per-hour billing, full SSH access, and ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Specifications
VRAM per GPU
Architecture
Compute capability
FP32 compute
Memory bandwidth
Power limit
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Available configurations
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What the A800 PCIE is good for
Large Language Model Inference
Host large language models entirely in the 80 GB VRAM buffer using pre-configured vLLM or Ollama templates.
Deep Learning Fine-Tuning
Train and fine-tune models with large batch sizes and long context windows in PyTorch/CUDA without running into memory bottlenecks.
High-Throughput Batch Processing
Utilize about 1598 GB/s of memory bandwidth to process data-intensive neural network workloads and embeddings.
Complex Generative Workflows
Run multi-model diffusion pipelines in ComfyUI while keeping multiple checkpoints loaded in memory simultaneously.
Is it the right GPU for you?
Choose the A800 PCIE when your primary requirement is 80 GB of memory capacity for large-scale AI models rather than raw single-precision compute. If your workload fits into a smaller memory footprint, a lower-VRAM workstation or consumer GPU is more appropriate, whereas compute-bound tasks may benefit from newer generation datacenter architectures.
Compare with similar GPUs
Frequently asked questions
Is the NVIDIA A800 PCIE suitable for large language model workloads?
Yes, its 80 GB of VRAM and about 1598 GB/s of memory bandwidth make it well-suited for serving and fine-tuning large language models that exceed the capacity of standard consumer cards. It supports CUDA compute capability 8.0 for broad compatibility with modern AI frameworks.
How does this GPU compare to smaller or newer datacenter models?
The A800 PCIE offers a massive 80 GB memory pool on the Ampere architecture, making it ideal for memory-bound tasks compared to lower-capacity cards. However, with about 15.6 TFLOPS of single-precision performance, workloads that rely heavily on raw compute rather than memory capacity may run faster on newer architecture generations.
How do I connect to and configure my A800 PCIE instance?
Every instance on ONIE includes direct SSH access for full control over your environment. You can also launch immediately using ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
How does billing work for renting the A800 PCIE?
ONIE uses straightforward per-hour billing for all GPU instances. You only pay for the time your instance is active, allowing you to scale your datacenter compute usage as needed.
Rent the A800 PCIE by the hour
Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.
Rent A800 PCIE