NVIDIA
RTX A6000
Provider Marketplace
All Cloud Providers
Estimates only — rates are collected automatically from public provider pages and may be out of date. Prices vary by region, commitment term, and availability, and typically exclude storage, egress, and tax. Confirm current pricing with the provider before purchasing. Last collected .
Compute Performance
Architecture
Memory & VRAM
Connectivity & Scaling
Virtualization
Power & Efficiency
Physical Design
Software Ecosystem
Server & Deployment
System Compatibility
Benchmarks & Throughput
Structured Sparsity
Hardware support for structural sparsity doubles the throughput for inferencing.
Training Benchmarks
New Tensor Float 32 (TF32) precision provides up to 5X the training throughput over the previous generation to accelerate AI and data science model training without requiring any code changes.
Scaling Efficiency
With up to 112 gigabytes per second (GB/s) of bidirectional bandwidth and combined graphics memory of up to 96GB, professionals can tackle the largest rendering, AI, virtual reality, and visual computing workloads.
Multi-GPU Scalability
Scaling Efficiency
Scaling Characteristics
Workload Readiness
LLM Training
up to 5X the training throughput over the previous generation
LLM Inference
doubles the throughput for inferencing
HPC / Simulation
significant performance improvements for graphics and simulation workflows (including CAE)
Scientific Computing
designed for scientists to meet compute-intensive workflows
Market Authority
Community Benchmarks
SPECviewperf 2020; Autodesk VRED; BERT Large Training
Enterprise Cases
Predator Cycling; Archilime; David Baylis (Real-Time Automotive Rendering)
Key Strengths
Limitations
Also in the Lineup
Expert Insight
The RTX A6000 represents a strategic leap in AI compute. When comparing cloud providers, consider not just the hourly rate, but also the interconnect bandwidth (InfiniBand/NVLink) and regional availability which can significantly impact total cost of ownership for large-scale training.