The A10 is a high-performance datacenter GPU. Featuring 24GB of ultra-fast memory, it is engineered for the most demanding AI model training, large language models (LLMs), and complex scientific computing.
Recommended Scenarios
Deep Learning
Model Inference
Video Encoding
Architecture
Ampere
VRAM Capacity
24GB
Bandwidth
600 GB/s
CUDA Cores
9216
FP16 Perf.
62.4 TFLOPS
Power (TDP)
150W
What Users Say
Real experiences from ML engineers and researchers
Inference microservicesReddit
"A10 is my go-to for inference microservices. At $0.60-0.80/hr, you get 24GB VRAM and solid FP16 performance. Runs BERT-large and GPT-J comfortably. Not as sexy as A100s but way more cost-effective for serving. We run 20+ A10 instances vs what would've been 5 A100s. Better utilization."
Stable Diffusion inferenceDiscord
"Honestly surprised A10s aren't more popular. They're basically RTX 3080s for data centers with proper support. I use them for stable diffusion inference — 24GB is enough for most models, and they're consistently available. Vast.ai has them for like $0.50/hr sometimes."
B2B API servicesHacker News
"A10 sits in a weird spot. Not cheap enough to replace 4090s for budget work, not powerful enough to replace A100s for training. But for 'serious' inference where you need ECC memory and data center reliability? It's the sweet spot. We use them for client-facing APIs where 4090s felt too risky."
Mixed training and inferenceReddit
"Trained a small transformer on A10s. It worked but was slow — Tensor Cores are weaker than A100. Wouldn't recommend for training unless you're budget constrained. But for inference? Perfect. We get 500 tokens/sec on Llama 7B with vLLM. More than fast enough for most apps."