Rent NVIDIA A10G in the Cloud

NVIDIA A10G is a Ampere card with 24 GB of GDDR6 memory, 600 GB/s of bandwidth, and 70 TFLOPS of FP16 compute. It launched in 2021 and uses up to 300W of power.

On real workloads the memory subsystem is typically the most important factor: 600 GB/s of bandwidth is enough to sustain most transformer decoding tasks without starving the compute units, while 24 GB of VRAM supports a wide range of model sizes, especially once quantisation is applied.

Check the NVIDIA A10G page for complete specifications and related GPU matchups.

NVIDIA A10G is a Ampere card offering 70 FP16 TFLOPS and 35 FP32 TFLOPS alongside 600 GB/s of memory bandwidth. That's enough compute to handle modern model training and real-time serving workloads at serious scale.

Benchmarks show NVIDIA A10G performs particularly well on transformer-style models where tensor cores are saturated by large MatMuls. Diffusion models, speech, and vision workloads also see strong speedups versus older generations. For latency-sensitive production real-time serving, NVIDIA A10G usually hits target token-per-second rates on large language models well above the 30-50 tok/s threshold most products aim for.

The NVIDIA A10G page has the complete datasheet and side-by-side comparisons.

NVIDIA A10G shines for Inference, graphics rendering, AI-accelerated workloads. The card's 24 GB of VRAM and Ampere-generation tensor cores make it a strong match for AI AI workloads that push batch sizes hard and benefit from modern mixed-precision support.

It's overkill for toy experiments and under-sized for the largest frontier models, but covers the vast middle: fine-tuning open-source transformers, training diffusion models, running batch real-time serving pipelines, and supporting interactive workflows that need more VRAM than a consumer card offers.

Full specs, benchmarks, and comparisons are on the NVIDIA A10G page.