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The NVIDIA A40 brings Ampere architecture to professional visualization and AI workloads with 48GB of GDDR6 ECC memory. Designed for datacenters requiring both graphics and compute capabilities, the A40 offers a versatile platform for virtual workstations, rendering, and AI inference. For CUDA developers, the A40 provides a robust platform with ECC memory for reliability, vGPU support for virtualization, and strong Tensor Core performance for AI workloads. Its 300W power envelope fits standard datacenter infrastructure. This guide covers the A40's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance.
| Architecture | Ampere (GA102) |
| CUDA Cores | 10,752 |
| Tensor Cores | 336 |
| Memory | 48GB GDDR6 ECC |
| Memory Bandwidth | 696 GB/s |
| Base / Boost Clock | 1305 / 1740 MHz |
| FP32 Performance | 37.4 TFLOPS |
| FP16 Performance | 149.6 TFLOPS |
| L2 Cache | 6MB |
| TDP | 300W |
| NVLink | No |
| MSRP | $5,000 |
| Release | October 2020 |
This code snippet shows how to detect your A40, check available memory, and configure optimal settings for the Ampere (GA102) architecture.
import torch
import pynvml
# Check if A40 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# A40 Memory: 48GB - Optimal batch sizes
# Architecture: Ampere (GA102)
# CUDA Cores: 10,752
# Memory-efficient training for A40
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ampere (GA102)
torch.backends.cudnn.allow_tf32 = True
# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 48 GB total")
# Recommended batch size calculation for A40
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (48 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for A40: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Inference (imgs/sec) | 5,200 | TensorRT INT8 |
| Stable Diffusion (sec/img) | 5 | FP16 mode |
| LLaMA-7B (tokens/sec) | 45 | INT8 quantized |
| SPECviewperf 3dsmax | 120 | Professional rendering |
| Blender Rendering | 1.5x RTX 3090 | Cycles RT |
| Memory Bandwidth (GB/s) | 660 | 95% efficiency |
| Use Case | Rating | Notes |
|---|---|---|
| Virtual Workstations | Excellent | vGPU and ECC for enterprise VDI |
| Professional Visualization | Excellent | RT cores + 48GB for rendering |
| AI Inference | Good | Solid but L40S is faster |
| Mixed Graphics+AI | Excellent | Balanced capabilities |
| ML Training | Fair | Prefer A100 for training |
| Content Creation | Excellent | Rendering + video encoding |
L40 is the newer Ada-based successor with 2x faster performance. Choose A40 only if you need lower cost, specific compatibility, or availability. For new deployments, L40 is recommended.
A40 is decent for inference with 48GB memory and Tensor Cores. For training, A100 is significantly better. For inference-focused workloads, L40S offers better performance per dollar.
Yes, A40 has ECC GDDR6 memory which provides error correction for reliability-critical workloads. This is important for scientific computing and enterprise deployments.
Yes, A40 has excellent vGPU support for creating virtual workstations. It can be partitioned to serve multiple users for VDI deployments with GPU acceleration.
Newer Ada, 2x faster
Pure compute, HBM2e
Workstation, NVLink
Consumer, similar compute
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