NVIDIA A100
An NVIDIA data-center-grade AI training accelerator based on the Ampere architecture. The A100 has been added to the United States government's export control list restricting sales to China.
Core Facts
A single NVIDIA A100 card has 80GB HBM2e memory and 312 TFLOPS of FP16 computing power
The Tensor Core of the A100 can complete one 4×4 matrix multiply-accumulate operation within a single clock cycle.
The NVIDIA A100 has 6,912 CUDA cores and 432 Tensor Cores, with a theoretical peak compute of 312 TFLOPS (BF16).
Timeline (last 90 days)
The Gemma open-source model achieved 2.85x end-to-end inference acceleration on A100 via vLLM, with throughput increasing from about 37,900 tokens/second to over 108,000 tokens/second, while maintaining 100% token consistency and zero quality degradation.
Four NVIDIA A100 80GB GPUs interconnected via NVLink can theoretically provide 320 GB of aggregate VRAM.
In a data center, a single A100 can handle almost any CUDA workload, allowing compute resources to be pooled, uniformly scheduled, and freely allocated.
The same A100 chip can continuously gain performance improvements through ongoing software-level optimizations such as operator libraries, compilers, and kernel scheduling.
NVIDIA officially defines the A100 as remaining mission-ready from 2020 through 2029.
The United States has attempted to establish a technological barrier at the hardware level by restricting the export of high-end AI chips (such as the NVIDIA H100 and A100) to specific countries.
The monthly cost of locally deploying a server equipped with an NVIDIA A100 high-end GPU is approximately $2,000–$5,000, while GPT-4o costs approximately $2.50 per million input tokens.
Modern AI service providers typically deploy clusters of hundreds to thousands of GPUs (such as NVIDIA A100s and H100s) and use orchestration systems such as Kubernetes for resource scheduling.
Mainstream GPU inference service prices using NVIDIA A100 or H100 instances typically range from $2 to $8 per hour.
Using GPU cloud providers such as Lambda Cloud and CoreWeave, the monthly rental cost for a single A100 can be as low as $1,000–$1,500.
3 more timeline events
All Facts (15)
A single NVIDIA A100 card has 80GB HBM2e memory and 312 TFLOPS of FP16 computing power
90%VerifiedThe Tensor Core of the A100 can complete one 4×4 matrix multiply-accumulate operation within a single clock cycle.
80%VerifiedThe NVIDIA A100 has 6,912 CUDA cores and 432 Tensor Cores, with a theoretical peak compute of 312 TFLOPS (BF16).
75%VerifiedIn October 2022, the Biden administration introduced its first export controls targeting advanced computing chips, restricting the export of high-end GPUs such as the NVIDIA A100 and H100 to China.
75%Unverified传统GPU如NVIDIA A100/H100在推理阶段面临内存带宽瓶颈，模型权重需要在每次生成Token时从显存反复读取
90%UnverifiedIndustry estimates suggest GPT-4's training used approximately 25,000 NVIDIA A100 GPUs over several months, with compute costs alone exceeding $100 million
60%UnverifiedFull deployment of DeepSeek-V3.2 typically requires a cluster of multiple high-end GPUs (such as NVIDIA A100/H100)
60%UnverifiedThe Gemma open-source model achieved 2.85x end-to-end inference acceleration on A100 via vLLM, with throughput increasing from about 37,900 tokens/second to over 108,000 tokens/second, while maintaining 100% token consistency and zero quality degradation.
50%UnverifiedFour NVIDIA A100 80GB GPUs interconnected via NVLink can theoretically provide 320 GB of aggregate VRAM.
50%UnverifiedNVIDIA officially defines the A100 as remaining mission-ready from 2020 through 2029.
50%UnverifiedThe same A100 chip can continuously gain performance improvements through ongoing software-level optimizations such as operator libraries, compilers, and kernel scheduling.
50%UnverifiedIn a data center, a single A100 can handle almost any CUDA workload, allowing compute resources to be pooled, uniformly scheduled, and freely allocated.
50%UnverifiedThe United States has attempted to establish a technological barrier at the hardware level by restricting the export of high-end AI chips (such as the NVIDIA H100 and A100) to specific countries.
50%UnverifiedModern AI service providers typically deploy clusters of hundreds to thousands of GPUs (such as NVIDIA A100s and H100s) and use orchestration systems such as Kubernetes for resource scheduling.
50%UnverifiednanoGPT速通竞赛中参与者竞相在NVIDIA A100或H100等GPU上以最短wall-clock时间达到特定验证集loss
50%