A10 GPU
The A10 balances AI inference and graphics performance in a power-efficient package, making it suitable for medium-scale AI workloads and virtualized environments. It offers solid compute and memory capacity without the higher cost of top-tier GPUs.

Cloud Pricing
Cheapest on Vast.ai — 91% below avgPrices updated daily. Last check: May 13, 2026
Performance
Strengths & Limitations
Strengths
- 24GB GDDR6 memory provides substantial capacity for large models and datasets
- Single-slot form factor enables higher density server configurations
- 150W TDP offers efficient power consumption relative to performance
- 288 Tensor cores with multiple precision support (TF32, BFLOAT16, FP16, INT8, INT4)
- 600GB/s memory bandwidth supports memory-intensive applications
- NVIDIA vGPU software compatibility enables virtualized GPU sharing
- PCIe Gen4 support provides 64GB/s interconnect bandwidth
Limitations
- 150W power draw may require adequate cooling in dense deployments
- Ampere architecture lacks some newer features found in Ada Lovelace and Hopper generations
- Graphics-focused positioning may be overkill for pure AI inference workloads
- Lower compute density compared to dedicated AI accelerators like H100 or newer GB300 series
- Released in 2021, representing previous-generation technology compared to current offerings
Key Features
About A10
Common Use Cases
The A10 is well-suited for hybrid workloads that combine professional graphics and AI inference, making it ideal for virtual desktop infrastructure serving CAD applications, architectural visualization, and content creation workflows. Its 24GB memory capacity and Tensor core capabilities support AI inference for computer vision, natural language processing, and recommendation systems at moderate scale. The GPU's support for NVIDIA vGPU software makes it particularly valuable in multi-tenant cloud environments where GPU resources are shared across multiple users running graphics-intensive applications or AI workloads that don't require the full compute power of larger data center GPUs.
Full Specifications
Hardware
- Manufacturer
- NVIDIA
- Architecture
- Ampere
- CUDA Cores
- 9,216
- Tensor Cores
- 288
- RT Cores
- 72
- Process Node
- 8nm
- TDP
- 150W
Memory & Performance
- VRAM
- 24GB
- Memory Interface
- 384-bit
- Memory Bandwidth
- 600 GB/s
- FP32
- 31.2 TFLOPS
- FP16
- 125 TFLOPS
- BF16
- 125 TFLOPS
- INT8
- 250 TOPS
- Release
- 2021
Frequently Asked Questions
How much does an A10 cost per hour in the cloud?
A10 pricing varies by provider, region, and commitment level. Check the pricing table above for current rates across all providers.
What is the A10 best used for?
The A10 excels at hybrid workloads combining professional graphics and AI inference, including virtual desktop infrastructure for CAD applications, architectural visualization, moderate-scale AI inference, and multi-tenant environments requiring GPU virtualization through NVIDIA vGPU software.
How does the A10 compare to newer GPUs for AI workloads?
While the A10's 288 Tensor cores and 24GB memory handle AI inference well, newer architectures like Ada Lovelace and Hopper offer improved efficiency and features. The A10's strength lies in its dual-purpose design for graphics and AI, whereas dedicated AI accelerators like H100 or GB300 series provide higher compute density for pure AI workloads.