5 Best GPUs for Deep Learning in 2023

Deep learning has revolutionized fields like computer vision, natural language processing, and speech recognition. The computational power required to train deep neural networks has also grown tremendously. GPUs have emerged as the hardware of choice to accelerate deep learning training and inference. Selecting the right GPU is crucial to maximize deep learning performance. This article compares NVIDIA's top GPU offerings for deep learning - the RTX 4090, RTX A6000, V100, A40, and Tesla K80.

NVIDIA dominates the deep learning GPU market. Its CUDA parallel computing platform and cuDNN deep neural network library enable leveraging the immense parallel processing power of NVIDIA GPUs. Key factors to evaluate for deep learning workloads include tensor cores, CUDA cores, memory bandwidth, memory capacity, FP16/TF32/FP64 performance, PCIe bandwidth, power consumption, and price.

The NVIDIA RTX 4090, part of the GeForce series, is a gaming-focused GPU. With 16,384 CUDA cores and a 2.23 GHz boost clock, it delivers up to 2-4x the performance of the previous generation RTX 3090. However, its specifications make it a strong contender for deep learning applications. The RTX 4090 features massive 24GB GDDR6X VRAM with a 512-bit bus providing over 1 TB/s of bandwidth, which allows it to handle large datasets and complex models. It introduces fourth-generation tensor cores with FP8 precision and doubled FP16 throughput. This enables enhanced AI performance for deep learning training and inference. The RTX 4090's performance in deep learning tasks is impressive, thanks to its high memory bandwidth and vast CUDA core count.

However, the RTX 4090 is not specifically designed for deep learning, which means it lacks some features available in the other GPUs discussed here. For instance, it does not support NVIDIA's NVLink technology for multi-GPU scaling, which can be a critical factor in large-scale deep learning projects. Data center deployments should utilize professional GPUs like the A6000 instead.

The NVIDIA RTX A6000, part of the Ampere architecture, is a workstation GPU designed for professional and scientific computing. It features a colossal 48GB GDDR6 VRAM, which is twice the memory of the RTX 4090. This allows it to handle larger datasets and more complex models. The RTX A6000 also includes second-generation Ray Tracing cores and third-generation Tensor cores. The NVIDIA A6000 is an ampere-architecture professional GPU for data centers. It has 10,752 CUDA cores and 336 third-gen tensor cores. With a boost clock of 1.41 GHz, A6000 delivers up to 38.7 TFLOPs of FP16 tensor core performance. It has 48GB of ECC GDDR6 memory with a 384-bit bus providing up to 768 GB/s bandwidth.

In terms of performance, the RTX A6000 outperforms the RTX 4090 in deep learning tasks, thanks to its higher memory and Lower TDP. It also supports NVLink, allowing for effective multi-GPU scaling.

The A6000 utilizes PCIe Gen 4 with 32 lanes, delivering 128 GB/s bi-directional bandwidth to the host CPU. It supports multi-instance GPU partitioning, allowing up to 7 separate users or jobs per A6000 card. The A6000 has a 250W TDP and requires only a single 8-pin power connector. The A6000 excels at inferencing workloads and model training at small batch sizes.

NVIDIA’s A40 is built on the Ampere architecture and positioned between the A100 and A10 for scale-up servers. The A40 has 10,752 CUDA cores and 420 third-gen tensor cores. It delivers 37 TFLOPs of FP16 or 37 TFLOPs of FP32 performance.

The A40 supports third-generation NVLink interconnecting up to 8 GPUs. It has a PCIe Gen4 x16 interface with 64 lanes of bandwidth to the host CPU. The A40 supports concurrent kernels, allowing different types of workloads to run simultaneously on the GPU. It has a 300W TDP and requires two 8-pin power connectors.

The NVIDIA A40 is a data center GPU designed for AI and high-performance computing. It features a massive 48GB GDDR6 VRAM with 672 GB/s bandwidth. The A40 also includes third-generation Tensor cores, which are designed to accelerate AI tasks.

In terms of performance, the NVIDIA A40 is among the most powerful GPUs available for deep learning. It outperforms the V100 and matches the RTX A6000 in most metrics, thanks to its high memory and CUDA core count. The A40 GPU's performance is close to its workstation peer, the A6000 which is about 10% faster due to a slightly higher clock speed and memory bandwidth. However, the A40 is better suited for use in servers since it is passively cooled.

NVIDIA’s Tesla V100 uses the Volta architecture and was released in 2017. It quickly became the gold standard GPU for accelerating deep learning and HPC workloads. The V100 has 5,120 CUDA cores and 640 tensor cores with FP16/FP32/FP64 capabilities. It delivers up to 28 TFLOPs of FP16 or 14 TFLOPs of FP32 performance.

The Tesla V100 has 16GB or 32GB of HBM2 memory delivering 900 GB/s of bandwidth. It supports NVLink interconnect for multi-GPU model training. The 250W V100 requires two 8-pin power connectors. The V100 has excellent mixed-precision capabilities ideal for deep learning training. It continues to be widely used in academia and companies on GPU clusters, despite being an older generation.

In terms of performance, the V100 offers excellent deep learning computational power. However, it falls behind the RTX 4090 and RTX A6000 in terms of raw specifications.

The Tesla K80 dual-GPU is a relic from NVIDIA’s Kepler generation launched in 2014. Each of the two GK210 GPUs has 2,496 CUDA cores delivering up to 2.7 TFLOPs of double precision performance. The K80 introduced GPU Boost Technology automatically increasing clock speeds based on thermal headroom.

The K80 has 12GB GDDR5 memory per GPU delivering up to 480 GB/s bandwidth. It uses PCIe Gen 3 x16 interface with 16 lanes connecting to the CPU. The card has a TDP of 300W and needs two 8-pin power connectors. Given its dated architecture and performance, the K80 is only recommended for academic research labs on a tight budget.

For deep learning workloads, the A6000 delivers the best performance but carry a high price tag. The new RTX 4090 offers unmatched value for cost but is not suitable for data centers. The A40 provides a balanced middle-ground combination of price and capabilities. For cost-sensitive academic research, refurbished V100 or K80 GPUs could be considered. Production systems should utilize latest generation data center GPUs like the A6000, A40. The A40 offers a cost-effective inference solution. Carefully evaluating compute requirements, and budget will help choose the right GPU for your deep learning needs.

Keep in mind that the hardware is just one piece of the puzzle in deep learning. Equally important is the software stack (frameworks, libraries, drivers), which must be well-optimized for the chosen hardware. NVIDIA provides a comprehensive software ecosystem, including CUDA for programming, cuDNN for deep neural networks, and TensorRT for inference optimization, which are compatible across all these GPUs. Remember, there is no 'one-size-fits-all' answer to the best GPU for deep learning. It's about finding the right balance between performance, price, and your specific needs.