GPU vs FPGA: Which is Best for Your Machine Learning Applications?

Jason Karlin

Last Updated: Sep 11, 2025

8 Minute Read

1419 Views

GPU vs FPGA: Which is Best for Your Machine Learning Applications?

In this blog we will discuss which is best for your Machine Learning i.e., GPU vs FPGA. It is no wonder then that Machine Learning (ML), with its ability to comprehend rapidly fluctuating data and adapt the modus operandi in line with shifting business goals, has become foundational for tech stacks across industries.

If you’re in the market to tap into the glorified potential of ML to achieve lucrative business results, you definitely would have heard about GPU and FPGA. These are the foremost technology tools that can power your resource-intensive, machine learning applications efficiently and optimally. Choosing between them is not really a herculean task, but extensive research is required before making an informed decision.

We are here to help you with that.

GPU vs FPGA: How is FPGA different from a GPU?

GPU (Graphic Processing Unit), as the name suggests, is a processor – a specialized processor! It was built to support graphics rendering workloads. In today’s world of burgeoning graphical applications requiring ultra-high resolution, 3D simulation and image recognition, the GPU is revolutionizing the landscape.

But it also supports the CPU in general-purpose computing and is increasingly being used for efficient parallel calculations, such as those that comprise AI/ ML model training. Powered by its remarkable Single Instruction Multiple Data architecture (SIMD), it is adept at High-Performance Computing (HPC). It is, therefore, being used in supercomputers involved in comprehensive data modelling and sophisticated mathematical calculations running parallelly.

[acecloud_popup_cta title=’Maximize Efficiency And Scale Up Your Business with
AceCloud’ button_text=’Know More’ form_id=10]

Over the last few years, it has evolved into newer versions embedded with GPU APIs such as Compute Unified Device Architecture (CUDA). This is where we mark the starting point for the development of deep and extensive neural networks and their libraries. In a nutshell, GPUs can now easily be reprogrammed to meet computational requirements.

Simplified bird’s eye view of FPGA architecture (Source)

Field-Programmable Gate Array (FPGA) is another semiconductor device which comprises of an array of logic blocks that can perform various logic functions, ranging from simple AND or NOR, to more complex combinational functions. These logic blocks are dynamically programmable post-manufacturing via reconfigurable wire-based interconnects. Together the logic blocks and wire interconnects make up the internal (reconfigurable) instruction routing landscape of the FPGA chip.

Thus, FPGA has the flexibility to accommodate a variety of compute-intensive workloads, ranging from medical imaging applications to hardware acceleration (identical to GPUs). Presently, they are almost exclusively being used for AI/ ML projects and developing highly customized, vertical software applications where minimal time-to-market is a necessary pre-requisite.

Is GPU better than FPGA? GPU vs FPGA performance comparison

Now that we’ve learnt a little about the physical architecture of GPU and FPGA, let’s compare the two from the ground up across performance parameters:

Parameter	GPU	FPGA	Winner
Architecture & Flexibility	Built for simultaneous execution of instructions which can be processed in parallel independent of each other. Comes with high memory bandwidth for quick access to information. Limited reprogramming given that computation is dependent upon (fixed) internal architecture. To tackle evolving workloads, additional GPUs are required. GPU is limited by its PCIe-only interface	Comparatively flexible architecture as it is easy to reconfigure the logic blocks. Given its ease of programmability, FPGA is the optimum choice for prototyping purposes. However, programmability means that FPGA requires manifolds more die space and power supply via-a-vis fixed design processors. Moreover, any errors during reprogramming/ fabrication can destroy the chip. Thus, FPGA is much more difficult to construct than GPU.	Draw! Wait till you get to the bottom!
Backward Compatibility	Old software versions developed for older GPU models continue to work even with new devices.	FPGA can also work easily with new platforms, however, needs to be reconfigured	GPU
Power Efficiency	GPU is power efficient when leveraging SIMD architecture. However, it demonstrates comparatively high power consumption when deployed for sequential execution or used for software programmability.	FPGA is more energy efficient vis-a-vis GPU and requires comparatively lower power supply. Therefore, it requires fewer countermeasures to aid thermal dissipation and can be deployed even in smaller settings and high temperature environments.	FPGA
Processing efficiency	GPU is less efficient than FPGA in terms of processing power per watt, i.e., it is less energy efficient.	FPGA is more energy efficient than GPU but lags drastically behind in terms of processing power per unit cost.	Draw – depends on the metric considered
Programming Language, Development & Ecosystem	GPU can be configured using general-purpose software programming languages, including C, C++, Java, Python, etc It is easier to design algorithms, CUDA is very easy to use, and developers need not have in-depth understanding of the underlying hardware. Hence, the developers also cost less comparatively. GPU has a mature support ecosystem and a burgeoning marketplace.	FPGA can be programmed using Hardware Description Languages (HDLs) such as VHDL and Verilog The developers must have knowledge of the underlying FPGA hardware as well as know ML algorithms. Hence, it is more difficult to design and deploy. Given the extensive knowledge requirement and labour-intensive fabrication process, using FPGA is exorbitant. FPGA developers are difficult to find and are expensive, which adds to the input cost. Because of the above-mentioned reasons, FPGA market and support ecosystem are still in the nascent phase.	GPU
Floating Point Operations	GPUs are designed to perform parallel processing of floating-point operations using thousands of small cores. The fastest GPU has a floating-point performance above 300 TFLOPS. Even mid-range GPUs deliver considerable TFLOPS performance.	FPGAs are optimised to perform concurrent fixed-point operations with a close-to-hardware programming approach. Many HPC applications, including ML, have strong dependency on floating point operations. FPGA fails to accelerate ML applications when used in sparse networks, even though it has demonstrated great potential in such sparse networks,	GPU
Latency	GPU is more dependent on continuous flow of training data and fetching of instructions from cache memory for repetitive, parallel processing.	FPGA’s architectural framework allows it to process applications immediately upon receiving data input. Thus, it can achieve significantly higher computation in minimal time.	FPGA

Choose GPU or FPGA? Let the Workload Determine!

If the decision to use GPU vs FPGA could rely only on the result of the stand-off between GPU and FPGA, it would have been easy! However, when we are analysing technology for uptake, considering use cases, and mapping it to our requirements, gives a clear anticipated path and makes way for data-oriented decision-making.

Hence do evaluate your requirements as the first step, primarily:

– Your computational requirements

– Your budget

Let’s consider the industry case of using GPU and FPGA to accelerate Apache Spark SQL for data querying workloads. Though this is not an apple-to-apple comparison, using Nvidia’s RAPIDS Opensource suite on GPU and Bigstream on FPGA, the key takeaways are:

ML model training – given that ML uses floating point operations and parallel computing, RAPIDS win hands down.
Performance speedup – RAPIDS average speedup was 1.9x while Bigstream accounted for 3.6x average speedup

Another workload that can be investigated is FPGA vs GPU for Deep Learning via object detection and recognition algorithm YOLO. The result favours FPGA both in terms of speed (fps) and power efficiency (fps/watt).

As a rule of thumb, GPU and FPGA are optimum for the below-mentioned applications respectively:

FPGA	GPU
Hardware development/ emulation	Graphics processing, 3D animation and simulation
Prototyping	Computer vision, object recognition
Real-time data acquisition	Data querying (bioinformatics, computational finance, etc)
Real-time image processing	Artificial Intelligence/ Machine Learning training
Robotics and motion control

Once your budget and computational requirement are defined and you’ve mapped them with the advantages and disadvantages of GPU and FPGA, you should be able to take a call on using either. Nonetheless, let us help you zero down on your decision.

Starting with GPU is considerably easier given its flexibility and highly efficient parallel processing capabilities. Retail GPU are more affordable than FPGA, but enterprise-class GPU like A100 is exorbitantly priced. In this case, it is often an excellent idea to subscribe to Cloud GPU services from reputable cloud service providers. Once you’ve begun working with a GPU and if your requirements evolve or still has gaps, opt for FPGA.

In short, GPU is cost effective, easy to use and excellently suited for parallelable, calculation-intensive workloads. FPGA, on the other hand, is customizable, energy efficient and dominates real-time data/ image processing. GPU does have one very important thing going for it, and that is outstanding vendor support and availability of Opensource libraries and API frameworks for different workloads.

Let us know whether you prefer GPU or FPGA in the comments below.

Jason Karlin

author

Industry veteran with over 10 years of experience architecting and managing GPU-powered cloud solutions. Specializes in enabling scalable AI/ML and HPC workloads for enterprise and research applications. Former lead solutions architect for top-tier cloud providers and startups in the AI infrastructure space.