Overview
FPGAs sit at a unique point in the AI hardware stack: software-definable silicon. Unlike fixed-function GPUs, FPGAs let you design custom data paths — ideal for workloads where every microsecond counts: autonomous vehicles, real-time video analytics, radar/sensor fusion, 5G signal processing, and scientific instrumentation.
Modern FPGAs from Xilinx (AMD) and Intel integrate dedicated AI engines and tensor blocks — delivering GPU-class throughput with FPGA-class determinism. QS Compute stocks development kits, accelerator cards, and production modules from both ecosystems. Price range: $500 to $15,000.
| Platform | AI Performance | Logic Cells | Interface | Form Factor | Price Range |
|---|---|---|---|---|---|
| Xilinx Versal AI Edge | Up to 228 TOPS | Adaptive SoC | PCIe Gen4/5 | Module / Dev Kit | $2,000–$8,000 |
| Intel Agilex 7 | Up to 40 TFLOPS FP16 | 2.7M LEs | PCIe 5.0, CXL | PCIe Card | $5,000–$15,000 |
| Lattice CrossLink-NX | Embedded Vision AI | 40K LUTs | MIPI D-PHY | Chip / Module | $500–$2,000 |
| Xilinx Alveo U280 | 24.5 INT8 TOPS | 1.1M LUTs | PCIe Gen4 x16 | FHFL PCIe Card | $4,000–$8,000 |
Product Line
Xilinx Versal AI Edge
Adaptive SoC with AI Engines delivering up to 228 TOPS. Integrated DSP, programmable logic, and dual Arm Cortex-A72/A53 application processors. Ideal for autonomous systems, ADAS, and real-time sensor fusion at the edge.
Intel Agilex 7 FPGA
Chiplet-based architecture with AI tensor blocks. Up to 40 TFLOPS FP16 throughput. PCIe 5.0 x16 with CXL support, 400G Ethernet hard IP. Built for data center AI acceleration, network offload, and signal processing at scale.
Lattice CrossLink-NX
Low-power FPGA optimized for embedded vision and edge AI. Hardened MIPI D-PHY, 2.5Gbps SERDES, Instant-on configuration in <10ms. Ideal for always-on camera AI in battery-powered devices.
Xilinx Alveo Accelerator Cards
Data center FPGA cards — U200, U250, U280 series. Up to 8GB HBM2. Programmable logic for custom AI/ML inference pipelines, real-time video transcoding, financial computing, and genomics acceleration.
Why FPGA for AI?
- Deterministic Latency — Microsecond-level inference. No scheduling variance. Critical for safety systems and real-time control loops.
- Power Efficiency — FPGAs often deliver better TOPS/watt than GPUs for specific inference workloads. Ideal for edge deployments with thermal constraints.
- Hardware Reprogrammability — Update your AI model AND your data path in the field. No silicon re-spin.
- Custom I/O — Direct sensor interfaces (MIPI, LVDS, JESD204B). No CPU/GPU bottleneck between sensor and compute.
Building a custom AI pipeline with FPGAs? Tell us your throughput target, latency budget, and interface requirements. We'll match the right silicon.
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