Introduction

AI systems are compute-intensive. Tasks like large-scale inference, model training, and real-time decision-making require powerful hardware. GPUs are central to this, accelerating workloads across every stage of the AI pipeline. NVIDIA’s Ampere architecture powers a range of GPUs built specifically for these needs, from efficient inference to large-scale training and enterprise computing.

The NVIDIA A10 and A100 GPUs are two of the most widely used options for running modern AI workloads. Both are based on the Ampere architecture but are built for different use cases. The A10 is often used for efficient inference, while the A100 is designed for large-scale training and compute-heavy tasks.

In this blog, we’ll take a closer look at the key differences between the A10 and A100, their architectural features, and when to use each one. We’ll also touch on how to think about flexibility in GPU access, especially as more teams face challenges with limited availability and scaling reliably.

NVIDIA A10

The NVIDIA A10 is built on the Ampere architecture with the GA102 chip. It features 9,216 CUDA cores, 288 third‑generation Tensor Cores supporting TF32, BF16, FP16, INT8, INT4, and 72 second‑generation RT Cores for ray tracing. The card includes 24 GB of GDDR6 memory with 600 GB/s bandwidth. With a Thermal Design Power(TDP) of 150 W and a single-slot, passively cooled design, the A10 is optimized for servers where power and space matter.

Key strengths and ideal use cases:

• Inference for small to medium‑sized models
  Perfect for running models in the few‑billion parameter range—think Whisper, LLaMA‑2‑7B, Stable Diffusion XL and similar. Offers solid inference throughput at low cost.

• Efficient sparsity support
  With Tensor Core sparsity, you can nearly double inference performance for compatible models without increasing compute power.

• Strong performance‑to‑cost ratio
  Excellent balance of cost, power draw, and compute capability for workloads that do not require massive GPUs.

• Virtual GPU support
  Compatible with NVIDIA vGPU software to run multiple isolated GPU instances from a single card. Useful for virtual desktops or shared compute environments.

• Media decoding and encoding
  Includes one hardware encoder and two decoders, with AV1 support. Enables efficient video processing and analytics alongside AI pipelines.

• Compact and efficient deployment
  The passive cooling and single‑slot form factor allow high-density installations without needing high-end server infrastructure.

In short, the A10 offers pragmatic performance for running small to medium-sized models, enabling cost-efficient inference and media workflows with low overhead and solid flexibility.

NVIDIA A100

The NVIDIA A100 is built on the same Ampere architecture using the GA100 chip, manufactured at 7‑nanometer scale and featuring 6,912 CUDA cores. It offers up to 80 GB of HBM2e (High-Bandwidth Memory) with over 2 TB/s bandwidth—ideal for memory-heavy workloads and preventing data bottlenecks during large model training or scientific simulations.

It delivers 432 third‑generation Tensor Cores that support FP64, TF32, BF16, FP16, INT8, and INT4 precision. TF32 enables up to 20× faster training on AI workloads without any code changes. With structured sparsity enabled, inference performance can roughly double. The GPU has a 250 W thermal design power (TDP) and supports advanced interconnects like NVLink (600 GB/s bidirectional) and Multi-Instance GPU (MIG), which allows it to be partitioned into up to seven isolated GPU instances.

Use Cases for the A100

• Large-scale model training
  With its high memory bandwidth and NVLink support, the A100 is designed to train transformer models, large vision models, and speech systems across multiple GPUs.

• Enterprise-grade inference
  High throughput and low latency make it suitable for large model inference in areas like autonomous systems or intelligent recommendation platforms.

• High-performance computing (HPC)
  Supports double-precision FP64 workloads essential for scientific simulations such as weather forecasting, protein folding, and material science.

• Data analytics at scale
  Handles big data workloads like anomaly detection and fraud analysis in real time, thanks to its massive memory and compute capabilities.

• Natural Language Processing (NLP)
  Powers training and inference on large LLMs for tasks such as translation, summarization, and conversational AI.

The A100 is the go-to GPU for workloads that require maximum memory, interconnect bandwidth, and partitioning flexibility. It accommodates everything from massive multi-GPU training jobs to high-density, multi-tenant inference services—all on a single card.

Head-to-Head Comparison: Key Differentiators

Although both the A10 and A100 are built on NVIDIA’s Ampere architecture, they cater to distinct workload profiles:

Architecture and Core Specs

• A10 uses the GA102 GPU with 9,216 CUDA cores, 288 third-generation Tensor Cores, and 72 second-generation RT Cores.

• A100 is based on the larger GA100 GPU with 6,912 CUDA cores and 432 third-generation Tensor Cores.

Memory and Bandwidth

• A10 has 24 GB of GDDR6 memory at 600 GB/s bandwidth.

• A100 supports 40 GB or 80 GB of HBM2e memory with 1.55 TB/s (40 GB) to more than 2 TB/s (80 GB) bandwidth, which is critical for memory-heavy workloads.

Inference and Use Cases

• A10 performs well for small to medium-sized models (e.g., up to 7B parameter LLMs and diffusion models). Its GDDR6 memory and Tensor Cores with sparsity deliver strong inference throughput at lower cost.

• A100 excels at large-scale AI training, distributed inference, high-performance computing (HPC), and data analytics. NVLink and HBM2e make multi-node and multi-GPU workloads efficient.

Scalability and Multi-Tenancy

• A10 lacks NVIDIA’s Multi-Instance GPU (MIG) and NVLink features.

• A100 supports MIG (up to 7 partitions) and NVLink, enabling GPU sharing, isolation, and fast inter-GPU communication for distributed workloads.

Power and Deployment

• A10 consumes 150 W, fits in a single slot, and uses passive cooling, which is ideal for high-density, low-power server setups.

• A100 draws 250 W, occupies dual slots, and requires active or specialized cooling infrastructure.

Performance to Cost Trade-offs

• A10 offers excellent value for inference and media workloads, delivering strong throughput with lower total cost of ownership.

• A100 is a high-investment option best suited to compute- and memory-bound tasks, and is worth it when time-to-results and peak performance matter.

When to Choose Which

• Choose A10 for efficient inference on small-to-medium models, virtual desktops, media encoding and decoding, and server-friendly density.

• Choose A100 for large model training, HPC simulations, large-scale inference with latency targets, and flexible multi-tenant or distributed architectures using MIG or NVLink.

Scaling AI Workloads with Flexibility and Reliability

We have seen the difference between the A10 and A100 and how choosing the right GPU depends on your specific use case and performance needs. But the next question is—how do you access these GPUs for your AI workloads?

One of the growing challenges in AI and machine learning development is navigating the global GPU shortage while avoiding dependence on a single cloud provider. High-demand GPUs like the A100, with its superior performance, are not always readily available when you need them. On the other hand, while the A10 is more accessible and cost-effective, availability can still fluctuate depending on the cloud region or provider.

Clarifai’s Compute Orchestration helps solve this problem by giving you direct control over where and how your workloads run. You can choose from multiple cloud providers—AWS, GCP, Azure, Oracle, Vultr—or even your own on-prem or colo infrastructure. No lock-in. No waiting in queue.

You define the environment, pick the GPUs (A10, A100, or others), and Clarifai handles provisioning, scaling, and routing your jobs to the right compute. Whether you need cost-efficient inference or high-performance training, this approach gives you flexibility, and helps you scale without depending on a single vendor.

Conclusion

There’s no one-size-fits-all GPU. The choice between the NVIDIA A10 and A100 depends entirely on your workload type, performance needs, and budget.

The A10 is ideal for small to medium-sized models and everyday inference tasks. It handles image generation, video processing, and light training workloads well. It’s also more power-efficient and affordable, making it a solid choice for teams running cost-sensitive applications that don’t need the horsepower of a full-blown training GPU.

The A100 is built for high-end use cases like training large language models, running heavy compute jobs, or scaling across nodes. It offers significantly higher memory bandwidth and compute capacity, which pays off when working with large datasets or high-throughput pipelines.

For a breakdown of GPU costs and to compare pricing across different deployment options, visit the Clarifai Pricing page. You can also join our Discord channel anytime to connect with AI experts, get your questions answered about choosing the right GPU for your workloads, or get help optimizing your AI infrastructure.