NVIDIA A10 Tensor Core GPU: Enterprise-Grade Power for AI, Graphics, and Inference

NVIDIA A10 GPU: Specs & Highlights

Here’s a breakdown of what makes the A10 GPU stand out:

• Architecture & Core Configuration

• Built on NVIDIA’s Ampere architecture, featuring:
• 9,216 CUDA cores
• 288 third-generation Tensor Cores (supports TF32, BF16, FP16, INT8, INT4)
• 72 second-generation RT Cores for real-time ray tracing

• Memory & Bandwidth

• 24 GB GDDR6 VRAM
• Memory bandwidth of 600 GB/s, via a 384-bit interface

• Performance Metrics

• FP32: ~31.2 TFLOPS
• TF32: 62.5 TFLOPS (125 TFLOPS with sparsity)
• BF16: 126 TFLOPS (250 TFLOPS* sparsity)
• FP16: 165 TFLOPS (330 TFLOPS* sparsity)
• INT8: 250 TOPS (500 TOPS* sparsity)
• INT4: 500 TOPS (1000 TOPS* sparsity)

• Form Factor & Power

• Single-slot, full-height, full-length (FHFL) design
• Passive cooling (requires adequate system airflow)
• Power consumption: 150 W TDP
• PCIe Gen4 x16 interface (up to 64 GB/s) (Network Outlet, NVIDIA, NVIDIA).

• Enterprise Features

• Designed for vGPU workloads—supports NVIDIA RTX Virtual Workstation (vWS) and other virtualization technologies.
• Versatile enough to handle both graphics-rich tasks (e.g., CAD, rendering, VDI) and AI inference workloads (NVIDIA).

Why the A10 Stands Out: Value Compared to Other GPUs

vs. NVIDIA T4

• The A10 is significantly more powerful than the T4, offering:
• More CUDA and Tensor cores
• Far greater VRAM (24 GB vs ~16 GB)
• Nearly double the memory bandwidth
• On benchmarks (e.g., Whisper inference), the A10 is only ~1.2–1.4× faster but costs ~1.9× more per minute. However, it supports workloads that the T4 can’t handle due to limited memory or compute power.
• Bottom line: A10 is a robust upgrade when the T4 isn’t sufficient—not just for speed but for capability.

vs. NVIDIA A100

• The A100 is a heavyweight designed for large-scale training and memory-intensive workloads, with HBM2e memory and far higher bandwidth (~1.5–2 TB/s).
• The A10 offers a budget-friendly inference alternative with solid performance for smaller to medium AI models (e.g., Whisper, LLaMA‑2‑7B, Stable Diffusion).
• Ideal use case: If you're targeting smaller models and tight budgets, the A10 delivers high value without overkill.

vs. NVIDIA L40S

• L40S caters to high-end generative AI and large language model workloads with advanced scalability.
• The A10 remains a more cost-effective choice for mixed graphics and moderate AI workloads—balancing performance and affordability.

Summary: Why the A10 Offers Great Value

Use Cases: Where A10 Truly Shines

• AI Model Inference – Great for LLMs up to a few billion parameters, audio and image models.
• Virtual Desktop Infrastructure (VDI) – Run multiple virtual workstations for design, engineering, and collaboration.
• Hybrid Workloads – Ideal where workflows blend graphics, rendering, and AI—such as creative studios or enterprise visualization.
• Cost-Conscious Scaling – Offers a balanced, efficient solution when high-end models like A100 are overkill.

Final Thoughts

The NVIDIA A10 Tensor Core GPU delivers impressive multi-purpose performance—bridging the gap between dedicated inference cards and high-end compute GPUs. With solid CUDA and Tensor core counts, substantial VRAM, strong memory bandwidth, and enterprise-grade virtualization support, it’s tailored for organizations seeking both flexibility and value.

If you're deploying AI or graphics workloads in constrained environments where power, space, and budget are key, the A10 is a powerful ally that punches above its weight.