The A10 is an Ampere-series datacenter GPU well-suited to many model inference tasks, such as running seven billion parameter LLMs. However, AWS users run those same workloads on the A10G, a variant of the graphics card created specifically for AWS. The A10 and A10G have somewhat different specs — most notably around tensor compute — but are interchangeable for most model inference tasks because they share the same GPU memory and bandwidth, and most model inference is memory bound.
To attain the full power of a GPU during LLM inference, you have to know if the inference is compute bound or memory bound. Learn how to better utilize GPU resources.
This article compares two popular GPUs—the NVIDIA A10 and A100—for model inference and discusses the option of using multi-GPU instances for larger models.
This guide helps you navigate NVIDIA’s datacenter GPU lineup and map it to your model serving needs.
So what are reliable metrics for comparing GPUs across architectures and tiers? We’ll consider core count, FLOPS, VRAM, and TDP.
Which is the best GPU for AI training and AI art? We compare the price and specs of the NVIDIA T4 and the NVIDIA A10 GPUs to decide which is the best GPU for ML.
Horizontal scaling via replicas with load balancing is an important technique for handling high traffic to an ML model.
Instance sizing is complicated. In this post, we'll follow a few simple heuristics to select an appropriate instance size that can handle your model while minimizing compute cost.