Best GPU for AI/ML and Deep Learning in 2024: RTX 4090 vs. 6000 Ada vs A5000 vs A100 Benchmarks

As artificial intelligence (AI) and machine learning (ML) continue to grow, selecting the best GPU for deep learning in 2024 has become more critical than ever. NVIDIA stands out as the industry leader, offering a range of GPUs designed to handle complex computational tasks. From the powerful RTX 40-series (Ada Lovelace) to the professional RTX A-Series, NVIDIA has solutions tailored for every use case.

In this blog, we’ll dive into a comparison of NVIDIA's top GPUs: RTX 4090, RTX 6000 Ada, A5000, and A100, alongside benchmarks and real-world performance evaluations. Whether you're deciding between the RTX 4090 vs. A100, comparing the A6000 vs. 4090 for AI, or considering the RTX 6000 Ada vs. 4090 for deep learning, we’ve got you covered with data-driven insights.

Testing Setup

We utilized two ProX PC setups for accurate benchmarking:

 

 

 

 

 

 

 

 

 

 

1. ProX PC X5500:

   • CPU: AMD Threadripper Pro 5000WX-Series 5975WX (32-Core, 3.90 GHz)
   • Memory: 256 GB DDR4, 3200 MHz
   • Storage: 1TB PCIe SSD
   • OS: Ubuntu 20.04 with preinstalled deep learning frameworks

 

 

 

 

 

 

 

 

 

 

1. ProX PC ZX5500:

   • CPU: AMD Threadripper Pro 5995WX (64-Core, 3.5 GHz)
   • Memory: 256 GB DDR4, 3200 MHz
   • Storage: 1TB PCIe SSD
   • OS: Ubuntu 20.04 with preinstalled deep learning frameworks

We tested various models like ResNet-50, ResNet-152, Inception v3, Inception v4, and VGG-16 in both FP16 and FP32 configurations at maximum batch sizes.

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GPU Benchmark Results and Analysis

1. NVIDIA RTX 4090 (24 GB) – Price: ₹1,34,316

The RTX 4090 dominates as one of the best GPUs for deep learning in 2024. Its advanced Tensor Cores and high memory bandwidth make it highly effective for deep learning and AI tasks. In our tests, the 4090 deep learning performance was exceptional, though we noticed thermal issues in multi-GPU setups. Liquid cooling is a must to maintain stability, keeping temperatures between 50–60°C, compared to 90°C on air-cooled setups.

If you're comparing 4090 vs A100 or even RTX 4090 vs RTX 6000 Ada for deep learning, the RTX 4090 is ideal for researchers working with large models who require significant computational power.

2. NVIDIA RTX 6000 Ada (48 GB) – Price: ₹3,90,600

The RTX 6000 Ada is another strong contender, particularly when comparing RTX 6000 Ada vs 4090. The 6000 Ada offers enhanced memory and CUDA cores, making it perfect for handling large batch sizes during AI/ML tasks. It performed well in tasks demanding high memory but, like the 4090, benefits from liquid cooling to prevent thermal throttling during prolonged operations.

The RTX 6000 Ada is the go-to choice for high-end workstations or research labs requiring large memory and consistent performance.

3. NVIDIA A100 (80 GB) – Price: ₹11,75,916

For AI at scale, the A100 stands out, particularly for large-scale projects in data centers or advanced research. During our benchmarks, the A100 led in natural language processing, image recognition, and large-scale neural networks. If you're comparing 4090 vs A100 for deep learning, the A100 outperforms in terms of raw memory and multi-node capabilities, making it indispensable for complex deep learning tasks.

Its hefty price tag and resource demands make it more suitable for enterprise and large AI deployments rather than individual developers.

4. NVIDIA A5000 (24 GB) – Price: ₹2,60,400

A great middle-ground option is the NVIDIA A5000. For those comparing the A5000 vs 4090 in deep learning, the A5000 provides solid performance for medium-scale ML models. While it lacks the sheer power of the 4090 or A100, it’s far more budget-friendly and suitable for medium-level AI/ML applications. Liquid cooling isn't strictly necessary, but adding it can enhance long-term stability.

5. NVIDIA RTX 3090 (24 GB) – Price: ₹1,34,316

Despite being slightly older, the RTX 3090 remains a viable option for deep learning. When comparing 3090 vs 4090 deep learning, the RTX 4090 surpasses it in performance, but the 3090 is still suitable for users needing NVLink for extended memory (up to 48 GB). Like other GPUs, the 3090 performs best when paired with liquid cooling, reducing operating temperatures and prolonging GPU lifespan.

6. NVIDIA A6000 (48 GB) – Price: ₹3,90,600

The A6000 offers a substantial memory advantage over the RTX 4090 and is priced competitively for professionals who need more than the A5000 can offer but don’t require the A100. It’s ideal for running larger AI models, with exceptional performance across the board in tasks such as image recognition and deep learning. Liquid cooling is recommended for intensive workloads to keep the performance stable.

7. NVIDIA A4000 (16 GB) – Price: ₹1,09,200

For budget-conscious AI/ML developers, the NVIDIA A4000 provides an excellent entry-level solution. While its memory and computational power are lower than the A5000 or 4090, it's perfectly suited for smaller projects and lighter workloads.

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Recommended Configurations

If you're building an AI workstation or server, here are some optimal configurations to consider:

• ProX PC X5500: For multi-GPU setups with up to 4 RTX 4090s, ideal for heavy AI computations.
• ProX PC ZX5500: Supports up to 4 A100 GPUs and offers custom liquid cooling solutions for seamless operation.
• ProX PC ZX9000: An AI server with dual AMD EPYC processors and liquid cooling for 8 A100 GPUs, perfect for large-scale deployments.

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Conclusion: Which GPU is the Best for AI/ML in 2024?

Choosing the right GPU for AI and deep learning depends on your specific needs. Here's a quick guide:

• High-end GPUs (RTX 4090, A100): For large-scale models, data centers, and heavy computations.
• Mid-range GPUs (RTX 6000 Ada, A5000): Perfect for researchers or professionals who need solid performance without breaking the bank.
• Budget GPUs (A4000): Ideal for smaller workloads and those just getting started with AI/ML.

When considering long-term performance and stability, opting for liquid cooling solutions can help maximize GPU longevity and ensure peak performance during extensive workloads.

In conclusion, NVIDIA's 2024 GPU lineup offers the best-in-class solutions for anyone working in AI, deep learning, or ML. With ProX PC's deep learning workstations and AI servers, professionals can achieve maximum productivity and efficiency, regardless of project scale.

For more info visit www.proxpc.com

 

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