Rent NVIDIA RTX A6000 GPU by the hour
The NVIDIA RTX A6000 is an Ampere architecture workstation GPU equipped with 48 GB of VRAM and compute capability 8.6. It delivers about 36.1 TFLOPS of single-precision compute and about 651 GB/s of memory bandwidth within a 300 W power envelope. On ONIE, you can deploy this card with per-hour billing, full SSH access, and pre-configured templates.
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 RTX A6000 is good for
Large Language Model Inference
Run large language models using the vLLM or Ollama templates by leveraging the 48 GB memory capacity to hold sizable weights and context.
Deep Learning Fine-Tuning
Train and fine-tune neural networks in PyTorch/CUDA without frequent out-of-memory errors thanks to the generous workstation VRAM.
Generative Image Workflows
Execute complex diffusion pipelines and custom node graphs in ComfyUI while keeping multiple checkpoints loaded in the 48 GB buffer.
Workstation Rendering and Simulation
Process heavy scenes and compute tasks that benefit from Ampere architecture and about 651 GB/s of memory bandwidth.
Is it the right GPU for you?
Choose the RTX A6000 when your workloads exceed the memory limits of consumer cards and require 48 GB of VRAM for larger models or batch sizes. Opt for a smaller consumer GPU for lightweight inference, or step up to a newer architecture or datacenter GPU if your workflow requires higher memory bandwidth than about 651 GB/s.
Compare with similar GPUs
Frequently asked questions
Is the RTX A6000 suitable for LLM inference and fine-tuning?
Yes, its 48 GB of VRAM makes it well-suited for hosting and fine-tuning large models that do not fit on standard consumer cards. You can get started immediately using the ready vLLM, Ollama, or PyTorch/CUDA templates on ONIE.
How does this Ampere workstation card compare to newer or larger GPUs?
The RTX A6000 provides a large 48 GB memory buffer with compute capability 8.6 and about 36.1 TFLOPS of compute. If your workload is heavily compute-bound or bandwidth-bound rather than memory-capacity-bound, a newer generation workstation card or a datacenter GPU will offer higher throughput.
How do I connect to and manage my RTX A6000 instance?
All instances on ONIE provide direct SSH access for full control over your environment. You can also launch pre-built templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI to skip manual setup.
How does billing work for the RTX A6000?
Rentals on ONIE use per-hour billing, allowing you to pay only for the duration your instance is active.
Rent the RTX A6000 by the hour
Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.
Rent RTX A6000