Solutions for Large Model Inference Delays: A Comparative Guide to GPUs, TPUs, and FPGAs

Inference Delays in Large Language Models: The Root Cause is “Data Transfer”

One of the hottest topics in the AI industry today is the “inference speed of large language models (LLMs).” Many engineers are dissatisfied with the slow response times of conversational AI systems such as ChatGPT. However, surprisingly few people fully understand the root cause of these delays.

A common solution proposed is “use more powerful GPUs” or “add more memory.” However, these are merely surface-level remedies. Recent studies (such as the 60-page comprehensive survey paper, “Hardware Acceleration for Neural Networks,” published in 2026) reveal that the bottleneck in LLM inference isn’t computational power itself but rather memory bandwidth and data transfer efficiency.

Modern LLMs have reached a scale of hundreds of billions of parameters, requiring constant transfer of these massive weight parameters from memory to computation units during inference. In token-by-token generation, accessing the entire model’s weights for each token becomes necessary. If data transfer speeds are slow at this stage, high-performance computation units remain idle, resulting in increased latency and reduced overall throughput.

Comparison of Three Major Architectures: GPU, TPU, and FPGA

To address this challenge, three main hardware accelerators are currently in the spotlight. Understanding the characteristics of each is essential for making the right technological choices.

1. GPU (Graphical Processing Unit)

GPUs have been the mainstay of AI computation for years, excelling in parallel processing with thousands of small cores. NVIDIA’s CUDA ecosystem is well-established and offers extensive development tools. However, GPUs were originally designed for graphics processing and lack a memory architecture specifically tailored for LLMs. Recent models like the H100 and H200 employ HBM (High Bandwidth Memory) to significantly boost bandwidth, but the “general-purpose” design still introduces some overhead.

2. TPU (Tensor Processing Unit)

Developed exclusively by Google, TPUs are ASICs (Application-Specific Integrated Circuits) designed for tensor computations. TPUs are characterized by their optimized data flow from memory to computation units. The latest generations, such as TPU v5e and v6, incorporate features like sparse architectures optimized for LLM inference and technologies enabling direct access to large-scale memory. While primarily accessible through Google Cloud, TPUs have reportedly delivered 2-3 times better cost-performance ratios compared to GPUs for large-scale inference workloads.

3. FPGA (Field-Programmable Gate Array)

FPGAs are semiconductor devices that allow users to program their circuit configurations. Their greatest appeal lies in their customizability. Custom circuits tailored for LLM inference can be designed, enabling application-level optimization of memory access patterns and data flows. For example, Microsoft’s “Project Brainwave” uses FPGAs to achieve low-latency inference. However, the development costs and complexity are higher than GPUs or TPUs, requiring specialized knowledge in hardware description languages like VHDL or Verilog.

Industry Impact and Future Prospects

The diversification of technological choices is having significant implications for the AI industry.

Redefining Cost Efficiency: While performance was traditionally measured by FLOPS (floating-point operations per second), newer metrics like “inference cost per token” and “energy efficiency” are gaining importance. With data center energy consumption becoming a pressing issue, energy-efficient solutions like FPGAs are being reevaluated.

Expansion into Edge AI: For running LLMs on smartphones and IoT devices, stringent constraints on power consumption and size are driving increased adoption of FPGAs and custom ASICs. Companies such as Qualcomm and MediaTek are accelerating the development of mobile AI accelerators.

Co-Evolution of Software and Hardware: Frameworks like TensorFlow and PyTorch are evolving to automate optimizations that leverage hardware-specific features. Moving forward, collaboration between hardware designers and algorithm developers, often referred to as “codesign,” is likely to become standard practice.

Key Points for Engineers to Consider

How should engineers make their choices? Here is a framework to guide your decision-making:

1. Analyze Workload Characteristics: If batch processing is the focus, go for GPUs. If low latency is a must, consider FPGAs. For cost-optimized large-scale inference, explore TPUs.
2. Evaluate Ecosystem Support: Check if existing TensorFlow/PyTorch codebases can be leveraged and whether your team’s skillset aligns with the technology.
3. Calculate Total Cost of Ownership (TCO): Consider not just hardware purchase costs but also expenses for power consumption, maintenance, and development.
4. Assess Future Viability: Ensure alignment between the hardware roadmap and your organization’s AI strategy.

Conclusion: Rethinking the Definition of “Speed”

The issue of inference delays in large language models is not just a matter of comparing hardware performance—it challenges the design philosophy of entire systems. Recognizing the physical limitations of memory bandwidth and optimizing across three layers—algorithms, software, and hardware—is the true solution.

As of 2026, the AI hardware market is shifting from GPU dominance to a multipolar landscape. Engineers must resist chasing trends and focus on deeply understanding their specific use cases to select the most suitable “tool.” This choice will undoubtedly shape the future of the AI development race.

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