Top H100 Alternatives: Better Value for Your AI Workloads

If you’re tackling large-scale AI applications, the NVIDIA H100 GPU is likely on your radar, thanks to its exceptional computing power. But given its hefty price tag and long wait times, it’s no surprise that companies are looking for alternatives that won't stall their AI projects.

Thankfully, the market offers some strong options. AMD's MI300X provides a whopping 192GB of HBM3 memory and 5.3 TB/s of memory bandwidth (among other impressive features).

Intel’s Gaudi 2 is said to have a better performance-per-dollar ratio than the H100 in certain use cases. Even NVIDIA’s own lineup has contenders like the A100 and L40S, which balance power and affordability across diverse AI workloads.

This article examines four alternatives to NVIDIA’s H100 that could work better for your AI workload, whether you want faster delivery, better pricing, or specific performance gains.

Beyond NVIDIA H100: Why Consider Other AI GPU Options?

When NVIDIA launched the H100 Tensor Core GPU in 2022, it set a new benchmark for AI performance with its groundbreaking 80 billion transistors and 3.35 TB/s of memory bandwidth.

Even so, the H100 isn’t without its drawbacks. Companies are increasingly exploring alternatives to address issues like availability, cost, and workload-specific needs. Let's take a closer look.

High Cost: The Price of Power

The NVIDIA H100 is a premium GPU with a pretty significant price tag. While sources vary, estimates put the current price between $25,000 and $40,000 per unit. For large-scale AI workloads that need, say, 4 GPUs, that amounts to $160,000 in hardware alone.

When combined with infrastructure costs like cooling, power supplies, and networking, the total investment becomes expensive, to say the least.

In other words, the H100 may be out of reach for startups or growing companies with smaller budgets, especially if they need multiple GPUs for development, testing, and production environments.

Availability Challenges

Demand for the NVIDIA H100 far exceeds supply. In 2023, wait times exceeded a year due to overwhelming demand and production constraints. These delays forced companies to pause their AI workloads or run on older, less efficient hardware.

Currently, estimates put the wait times at 2 to 3 months, and NVIDIA's primary chip manufacturer, TSMC, plans to double its production capacity this year.

Even so, alternatives like AMD’s MI300X or Intel’s Gaudi 2 offer quicker access, making them attractive options for time-sensitive projects and buyers who can’t afford to wait.

Workload-Specific Requirements

Not every AI workload demands the same GPU capabilities. Some workloads benefit more from larger memory pools than raw compute power. Others might run better on GPUs optimized for specific operations like matrix multiplication or inference.

In other words, the H100’s design can either be overkill or poorly suited for certain use cases. It's, therefore, essential to choose a GPU that matches your specific workload so you get stellar performance without paying for more than you need.

H100 Alternatives for Tensor Core GPU

As promised, here are four standout alternatives to NVIDIA’s H100 Tensor Core GPU for your AI workloads.

AMD Instinct MI300X GPU: The Leading Alternative

The AMD MI300X is part of AMD’s Instinct™ series and is widely regarded as the best alternative to NVIDIA’s H100, especially for handling large-scale AI workloads.

Released in December 2023, this GPU packs 192GB of HBM3 memory, which is over twice the capacity of the H100 (80GB).

What’s more, the MI300X architecture combines 153 billion transistors with 5.3 TB/s of memory bandwidth, surpassing the H100's 80 billion transistors and 3.35 TB/s memory bandwidth.

At a glance, these benefits make the MI300X a more ideal choice than the H100 for memory-intensive tasks like training large language models (LLMs) and multi-modal AI systems.

Compatibility with industry-standard frameworks like PyTorch and TensorFlow is yet another advantage, letting developers integrate the MI300X smoothly into their existing AI workflows.

What Makes It Stand Out?

The MI300X stands out in several key areas:

• Higher Memory: The MI300X's larger memory handles bigger AI models without the performance penalties of data splitting across multiple GPUs. Our benchmarks at TensorWave found that the GPU shows particularly strong results with MoE models like Mixtral 8x7B, where memory capacity directly impacts processing speed.
• Energy Efficiency: Power efficiency is another advantage. The MI300X delivers its performance while staying within a 750W power envelope. This energy-efficient design helps reduce operational costs without compromising performance.
• Scalability: The MI300X also scales effortlessly from single-node setups to large clusters, allowing businesses to expand their AI infrastructure as their needs grow.

Key Considerations

Despite its impressive features, the MI300X is a relatively new product with limited market adoption compared to NVIDIA’s established offerings. Plus, while benchmarks of the MI300X show promising performance, real-world testing across different workloads is still ongoing.

Being newer also means fewer AI frameworks are optimized for AMD's ROCm architecture compared to NVIDIA’s CUDA ecosystem. Thankfully, there’s a straightforward workaround if you're ready to make the switch.

Pricing and Availability

Although the official pricing varies depending on vendor and volume, the MI300X typically costs between $10,000 and $20,000 per unit, which is notably less than the H100’s $25,000 to $40,000 price tag.

The MI300X availability is also a potential advantage—AMD has fewer reported supply chain delays compared to NVIDIA. With everything taken into account, the MI300X is worth serious consideration for companies seeking a powerful, cost-effective GPU for AI workloads.

Intel Gaudi 2

The Intel Gaudi 2 has positioned itself as another strong alternative to NVIDIA’s H100. Released as part of Intel’s efforts to expand its AI accelerators, Gaudi 2 is designed to optimize price-performance for AI training. It’s particularly well-suited for workloads involving generative AI models like GPTs and image synthesis systems.

The industry-standard MLPerf v4.0 benchmark results confirmed that the Gaudi 2 offers better performance per dollar than the H100. Another report from Stability AI found that Gaudi 2 can be faster than the H100 under certain circumstances.

What Sets It Apart

• Memory: The Gaudi 2 comes with 96GB of HBM2E memory and 48MB of SRAM with 2.45 TB/s memory bandwidth, translating to fast data processing for large models.
• Networking: Gaudi 2 integrates 24 on-chip 100GB Ethernet ports supporting RoCE v2, which delivers 2.4 TB/s total bandwidth. This built-in connectivity makes scaling across multiple units simpler and more cost-effective.
• Performance: Benchmarks from other companies like Databricks and ServeTheHome also found Intel Gaudi 2 to be a viable alternative to the H100 and other leading AI accelerators, especially when it comes to cost-effectiveness.

Key Considerations

While the Gaudi 2 excels in price-performance and scalability, its design caters primarily to specific workloads like LLM training and computer vision tasks.

In other words, the Gaudi 2 may not deliver the same level of versatility or raw power as NVIDIA’s H100 or AMD’s MI300X for more general-purpose AI tasks. This highlights a need to carefully evaluate your specific AI workload requirements before committing to Gaudi 2.

Pricing

During Intel’s keynote at Computex 2024, the company priced eight Gaudi 2 accelerators with a baseboard at $65,000, making each unit about $8,125 when purchased in this configuration.

This pricing structure—roughly one-third of the H100’s steep cost—makes Intel’s Gaudi 2 a budget-friendly alternative for teams building larger AI clusters.

NVIDIA A100 Tensor Core GPU

The NVIDIA A100 is built on the Ampere architecture and is designed to handle a wide array of AI workloads, from training and inference to general-purpose computing tasks.

This versatile GPU is particularly ideal for data-heavy applications, including scientific simulations, deep learning, and high-performance computing. It also consumes much less power than the NVIDIA H100 (400W versus 700W respectively).

What Sets It Apart?

The A100 SXM shines in both flexibility and performance. With 80GB of HBM2e memory and a bandwidth of 2 TB/s, it delivers strong performance across diverse workloads, making it a top choice for data centers and AI research.

Beyond raw performance, the A100’s mature software ecosystem means better reliability for production. At roughly half the H100’s price, it’s a compelling option for many AI projects and use cases.

Key Considerations

While powerful, the A100 is based on an older architecture, which may put it at a disadvantage in emerging AI workloads that are optimized for newer GPUs like the H100.

Pricing

Current market prices for the NVIDIA A100 vary from $18,000 to $20,000 for the 80GB SXM model.

NVIDIA L40S

Built on the Ada Lovelace architecture, the NVIDIA L40S combines AI capabilities with strong graphics performance.

Its 48GB of GDDR6 memory and 864 GB/s bandwidth can handle diverse workloads from AI inference to 3D graphics rendering like media production and simulation.

What Sets It Apart?

The NVIDIA L40S is highly efficient, with a power draw of just 350W, making it a more energy-conscious option.

It features 212 RT cores for enhanced 3D modeling and strong performance in parallel workloads like graphics and AI inference. The L40S is also more readily available than other high-demand GPUs like the H100.

Key Considerations

The L40S is best suited for workloads that combine AI with graphics or media needs. It’s not as powerful as the A100 or H100 for large-scale AI model training but excels in areas that balance moderate AI inference alongside graphics tasks.

Pricing

The price of the NVIDIA L40S GPU varies depending on the seller and region, with estimates falling between $7,600 and $9,800.

Experience the Power of AMD MI300X with TensoreWave

At TensorWave, we provide access to the powerful MI300X GPU, using its impressive memory and bandwidth to accelerate training, fine-tuning, and inference tasks.

With TensorWave, you can tap into the MI300X’s efficiency and performance to power your large-scale AI initiatives at a lower total cost of ownership. Our user-friendly, cloud-based infrastructure lets you test the GPU before committing and scale as your needs grow.

Consider TensorWave for your next AI deployment, and experience the full potential of the AMD MI300X. Get in touch today.

Key Takeaways

The NVIDIA H100 Tensor Core GPU is a popular choice of AI workloads today, and for good reason. Its cutting-edge specs make it a powerhouse for AI research and scientific simulations. But it’s not always the right fit.

Maybe it’s the steep price. Maybe it’s the availability issues. In any case, there are viable alternatives that don’t compromise quality. Here are four solid options to consider depending on your specific needs:

1. AMD MI300X: Doubles the H100’s memory at 192GB, making it ideal for large language models and memory-intensive AI workloads.
2. Intel Gaudi 2: Offers impressive price-to-performance value, especially in generative AI tasks where it matches or beats the H100.
3. NVIDIA A100: Provides proven reliability for diverse AI tasks, with strong software support and the ability to scale across multiple GPUs.
4. NVIDIA L40S: Balances AI performance with graphics capability in a power-efficient 350W package.

Need help choosing? Schedule a free demo with TensorWave today to get insights on the right GPU for your specific AI workload.