FPGA vs GPU for AI Inference: When FPGAs Actually Win

Q: Is an FPGA faster than a GPU for AI inference?

It depends on what you measure. For throughput on large batches a GPU is faster and cheaper. For the latency of a single small inference an FPGA can be far faster, because the model sits in the same circuit as the data interface and there is no trip to a host and back. FPGAs also give a fixed worst-case latency, which GPUs under load do not.

Q: When should I use an FPGA for inference?

Use one when the model has to sit in the path the data already travels: packets in a network card, samples from a converter, pixels from a sensor. Typical signs are a deadline under about one millisecond, a hard worst-case bound, a strict power limit, a custom interface, or a product life of ten years or more. Otherwise a GPU or NPU is the better choice.

Q: Can FPGAs run large language models?

Not competitively in production in 2026. Large language models are limited by memory bandwidth and by the volume of dense matrix arithmetic, and FPGA fabric spends much of its silicon on routing flexibility. Research demonstrations exist, but GPUs and purpose-built accelerators deliver far more tokens per dollar. Do not propose FPGAs as a way to cut language-model serving costs.

Q: What is the difference between an overlay and a dataflow FPGA design?

An overlay configures the FPGA as a programmable neural processor; models are compiled to its instructions in minutes and can be swapped without a hardware build. A dataflow design turns the model itself into a circuit, layer by layer, which gives nanosecond to microsecond latency but needs a multi-hour rebuild for every model change and a small, heavily quantised network.

Q: What is hls4ml?

hls4ml is an open-source tool that translates trained neural networks into FPGA firmware using high-level synthesis. It came out of particle physics, where trigger systems must classify collision data within microseconds in hardware. It produces dataflow designs in which each layer becomes dedicated logic, and it supports per-layer control of numeric precision and resource use.

Q: How much does a cloud FPGA cost compared with a GPU?

On the AWS on-demand price list for US East (N. Virginia), Linux, checked in November 2026, an f2.6xlarge with one FPGA is $1.98 per hour, an f2.12xlarge with two is $3.96, and an f2.48xlarge with eight is $15.84. A g6.xlarge GPU instance is $0.8048 per hour. The single-FPGA instance is therefore about 2.5 times the small GPU instance.

Q: Do I need FPGA engineers to deploy a model on an FPGA?

For overlay flows an ML engineer plus someone who can bring up a board is usually enough. For dataflow designs you need a digital design engineer who can read timing reports and manage resources, working closely with the ML engineer, because fixing a design that does not fit usually means changing the model. Staffing is the main cost of the project.

Q: Why is quantisation different on an FPGA?

On a GPU, quantisation is usually applied after training to save memory and speed up arithmetic. On an FPGA every bit of every weight and activation becomes logic and wiring, so precision is chosen per layer during training, often below eight bits. A network trained at six bits is a physically smaller and faster circuit than the same network at eight.

Satyam Kumar

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An FPGA Wins When the Model Sits in the Data Path: FPGAs for Inference Workloads in 2026

By Satyam KumarNovember 14, 202614 min read

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An FPGA Wins When the Model Sits in the Data Path: FPGAs for Inference Workloads in 2026

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Satyam Kumar

Founder & AI Architect, AppScale LLP

AI & Cloud Architect. Helping teams build systems that scale to millions.

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An FPGA Wins When the Model Sits in the Data Path: FPGAs for Inference Workloads in 2026

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