The A100 40GB is a high-performance datacenter GPU. Featuring 40GB 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
40GB
Bandwidth
1.5 TB/s
CUDA Cores
6912
FP16 Perf.
312 TFLOPS
Power (TDP)
250W
What Users Say
Real experiences from ML engineers and researchers
Production ML operationsReddit
"A100 is the Toyota Corolla of ML GPUs — not flashy, but it just works. We've trained hundreds of models on them over 2 years. Never had hardware failures. 80GB VRAM is perfect for most production LLM workloads. At around $1-1.50/hr, it's the sweet spot for serious work without H100 prices."
Inference serving for SaaSTwitter
"Still using A100s in 2024 and honestly? No regrets. For inference on 13B-30B models, they're perfect. We get better throughput per dollar than H100s for our use case. Unless you're training GPT-4 sized models, A100s are the pragmatic choice. Widespread availability is a plus too."
Computer vision researchHacker News
"The 40GB vs 80GB decision matters more than people think. We bought 40GB versions to save money and regret it constantly. Can't fit larger batch sizes, can't run bigger models. If you're getting A100s, pay the extra for 80GB. You'll thank yourself later."
Enterprise ML teamReddit
"A100s are everywhere for a reason. Every framework supports them, every provider has them, every bug is already documented. If you're building a team and need reliability over raw speed, A100s are it. But if you need the absolute fastest training? H100s are 2x faster now."
Startup fine-tuning Llama 2Discord
"Running 8xA100 on RunPod for $7.20/hr. It's been solid for fine-tuning Llama 2 70B. Had some networking hiccups initially but their support fixed it within hours. A100s aren't the newest anymore but they're proven. For startup budgets, they're the right call."