Mejores aceleradores de IA para modelos multimodales

Discover the most powerful and efficient AI accelerators specifically designed for multimodal models. These devices are crucial for optimising performance in tasks integrating text, image, and video, such as creative content generation or advanced data processing. We explore hardware solutions ranging from GPUs to specialised ASICs, ideal for developers, researchers, and companies looking to take their AI applications to the next level. Find the technology that will drive your multimodal artificial intelligence projects.

191100% verified
  1. 1

    Groq

    137 Global Votes
    • Offers fast inference and low latency

      (+4)

    Groq stands out for its LPU, which delivers inference speeds up to 20 times faster than traditional GPUs, crucial for multimodal models requiring real-time processing. Its platform supports advanced multimodal capabilities, including vision, audio, and text, enabling the development of complex AI agents and innovative applications.

  2. 2

    Axelera AI M.2 AI accelerator

    45 Global Votes
    • Best solution for AI acceleration

      (+4)

    This AI accelerator delivers up to 214 TOPS of inference performance with very low power consumption, making it exceptionally efficient for multimodal models at the edge. Its M.2 design and Metis AIPU technology enable significant acceleration for LLMs and VLMs, outperforming other solutions in its category.

  3. 3

    Azure Multimodal AI & LLM Processing Solution Accelerator

    7 Global Votes
    • Customizable code template for data processing pipelines

      (+4)

    This accelerator provides a customizable code template for building and deploying production-grade data processing pipelines with generative AI. It enables developers to integrate Azure AI services and LLMs to effectively handle multimodal data, speeding up the development of AI applications that understand text, tables, and charts.

  4. 4

    Google Eighth-Generation TPUs

    2 Global Votes
    • Powers the next era of AI

      (+4)

    Google's eighth-generation TPUs, TPU 8t and 8i, are engineered to power the next era of AI, offering 2-4x faster performance than their predecessors. They are crucial for the development and execution of advanced multimodal models, such as Gemini, and are optimized to accelerate the entire AI lifecycle, from frontier-model training to inference.

  5. 5

    SiliconFlow

    0 Global Votes
    • Lightning-fast AI platform

      (+4)

    SiliconFlow provides a lightning-fast AI platform that allows deploying, fine-tuning, and running over 200 optimized LLMs and multimodal models with simple APIs. It delivers up to 2.3× faster inference speeds and 32% lower latency compared to leading AI cloud platforms, making it a high-performance and cost-efficient solution for AI inference.

  6. All the rankings you can imagine

    Thousands of verified votes to discover the best. Your vote here counts

  7. 6

    Fireworks AI

    0 Global Votes
    • Uses state-of-the-art open-source LLMs and image models

      (+4)

    Fireworks AI provides a platform optimized for speed, quality, and cost in generative AI development, excelling in its ability to process unstructured data in real-time with high accuracy. It delivers significant performance improvements, such as 4x throughput and up to 50% latency reduction, making it ideal for demanding multimodal models.

Frequently asked questions

"This ranking evaluates platforms and hardware designed to optimize the performance of multimodal AI models, focusing on inference speed, memory capacity, bandwidth, and overall efficiency for complex tasks involving text, image, and video."
"Participation in this ranking is editorial. Platforms and hardware are selected based on their market relevance, technological innovations offered, and their impact on accelerating multimodal models, as highlighted in the industry context."
"The results of this ranking should be interpreted as a guide to the most prominent solutions in AI acceleration for multimodal models, considering factors such as training and inference performance, memory capacity, and support for large parameter models. They do not represent an absolute truth, but rather an insight based on available information."
"This ranking includes both hardware solutions, such as NVIDIA Blackwell GPUs, and software platforms and cloud services, such as SiliconFlow and Fireworks AI, which offer acceleration for inference, fine-tuning, and deployment of multimodal models."
"While performance and efficiency are the primary focuses, cost-effectiveness is mentioned in the context of some solutions, such as Fireworks AI, which promises 8x lower cost compared to other options, and SiliconFlow, which aims to be efficient and cost-effective. However, it is not the main evaluation criterion."

How we built this ranking and what to consider when choosing

"Our methodology for ranking the best AI accelerators for multimodal models is based on a comprehensive analysis of the technical capabilities and performance these solutions offer in the current landscape. The goal is to provide a clear guide for developers and enterprises looking to optimize their multimodal AI workloads."

  • "Performance Evaluation: Performance in training and inference tasks for multimodal models is considered, including metrics such as TFLOPS (Tera Floating Point Operations Per Second) and data processing speed."
  • "Memory Capacity and Bandwidth: The amount of HBM3e memory and associated bandwidth are key factors, as multimodal models are often memory-intensive."
  • "Support for Large-Scale Models: The ability of solutions to handle trillion-parameter models and extensive context windows is valued, which is crucial for advanced AI."
  • "Efficiency and Throughput: Resource utilization efficiency and overall throughput are analyzed, especially for generative AI applications that require low latency and high speed."
  • "Technological Innovation: Innovative features such as Tensor Cores, transformer engines, and chiplet designs that contribute to superior acceleration are considered."
  • "Solutions must demonstrate superior capability to accelerate both training and inference of multimodal AI models, including text, image, and video processing."
  • "Accelerators offering high memory capacity (e.g., HBM3e) and massive bandwidth are prioritized, as these are essential for handling large parameter models and data-intensive AI tasks."
  • "Platforms that provide high throughput and low latency are considered, which is crucial for real-time generative AI applications and enhanced user experiences."
  • "The ability to deploy, fine-tune, and run 200+ optimized models, including LLMs and multimodal models, through simple APIs is an important criterion for the inclusion of software platforms."
  • "The presence of advanced hardware features, such as next-generation Tensor Cores and transformer engines, that optimize performance for complex AI workloads is valued."