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The NVIDIA A10 serves as the mainstream datacenter GPU for AI inference and graphics, positioned between the low-power T4 and high-end A100. With 24GB GDDR6 memory, Ampere architecture, and 150W TDP, the A10 offers strong performance for cloud inference workloads. For CUDA developers, the A10 provides 3rd generation Tensor Cores with TF32 support and good inference throughput. Available across major cloud providers, it handles larger models than T4 while maintaining reasonable power consumption. This guide covers the A10's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing inference performance.
| Architecture | Ampere (GA102) |
| CUDA Cores | 9,216 |
| Tensor Cores | 288 |
| Memory | 24GB GDDR6 |
| Memory Bandwidth | 600 GB/s |
| Base / Boost Clock | 885 / 1695 MHz |
| FP32 Performance | 31.2 TFLOPS |
| FP16 Performance | 125 TFLOPS |
| L2 Cache | 6MB |
| TDP | 150W |
| NVLink | No |
| MSRP | $3,500 |
| Release | April 2021 |
This code snippet shows how to detect your A10, check available memory, and configure optimal settings for the Ampere (GA102) architecture.
import torch
import pynvml
# Check if A10 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# A10 Memory: 24GB - Optimal batch sizes
# Architecture: Ampere (GA102)
# CUDA Cores: 9,216
# Memory-efficient training for A10
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ampere (GA102)
torch.backends.cudnn.allow_tf32 = True
# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 24 GB total")
# Recommended batch size calculation for A10
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (24 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for A10: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Inference (imgs/sec) | 6,500 | TensorRT INT8 |
| BERT-Large Inference (sentences/sec) | 1,800 | 2x faster than T4 |
| Stable Diffusion (sec/img) | 5.5 | FP16 mode |
| LLaMA-7B (tokens/sec) | 30 | INT8 quantized |
| Video Transcoding (fps) | 180 | 1080p HEVC |
| Performance per Watt | 1.67 TOPS/W | Good efficiency |
| Use Case | Rating | Notes |
|---|---|---|
| Cloud Inference | Excellent | Mainstream cloud inference choice |
| Media Processing | Excellent | Strong encode/decode |
| AI Inference | Good | 24GB handles medium models |
| Virtual Workstations | Good | Graphics + compute |
| ML Training | Fair | Small models only |
| LLM Inference | Good | Up to 7B-13B quantized |
L4 is the newer Ada-based GPU with FP8 support, 48MB L2 cache, and similar performance at lower power (72W vs 150W). Choose L4 for new deployments, A10 only for cost or compatibility reasons.
A10 is approximately 2x faster than T4 with 24GB vs 16GB memory. It uses more power (150W vs 70W) and costs more. For larger models or higher throughput, A10 is worthwhile.
Yes, A10 runs Stable Diffusion well at about 5.5 seconds per image. The 24GB handles SDXL. For production, consider L4 for better performance per watt.
Yes, A10 is available on AWS (g5 instances), Google Cloud, Azure, and other providers. It is one of the most widely available datacenter GPUs for inference.
Next gen Ada, FP8, same power
70W, 16GB, lower performance
48GB, 300W, graphics focus
Consumer alternative
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