Alternative to GPU Inference

FPGA-accelerated inference delivers lower cost, deterministic latency, and an OpenAI-compatible API — without GPUs.

The Problem with GPU Inference

GPU-based inference works well at high batch sizes and for training workloads. But for many production inference scenarios, GPUs have structural inefficiencies:

• Queuing delays — GPU inference servers batch requests for throughput, introducing variable latency. Your P99 latency depends on what other requests are in the queue.
• Underutilization at low batch — A single inference request uses a fraction of an H100's compute capacity, but you still pay for the full GPU's power draw and memory.
• Fixed architecture — GPUs use the same SIMT architecture for every model. A 7B parameter model runs through the same compute fabric as a 405B model, with no hardware-level optimization.
• CUDA lock-in — The GPU inference stack depends on NVIDIA's proprietary CUDA runtime, cuDNN, and TensorRT. This creates vendor dependency and limits optimization flexibility.
• Supply constraints — H100 and H200 GPUs face ongoing supply shortages. Memory (HBM2E) costs are rising, and allocation priority goes to the largest cloud providers.

How FPGA Inference Is Different

FPGAs (Field-Programmable Gate Arrays) are reconfigurable chips. Instead of running software on fixed hardware, you configure the hardware itself to implement the inference pipeline. This means:

Side-by-Side Comparison

When to Use FPGA Inference

FPGA inference is the better choice when:

• Latency consistency matters — Real-time applications, interactive agents, trading systems, or any workload where tail latency spikes are unacceptable.
• Cost is a priority — High-volume inference where 40-60% savings compound significantly.
• Low batch sizes — Single-request or small-batch inference where GPUs are underutilized.
• Energy constraints — Edge deployments, sustainability requirements, or data centers with power limits.
• NVIDIA supply risk — Diversifying away from a single hardware vendor.

When to Stay on GPUs

GPUs remain the better choice for:

• Training — FPGAs are not designed for model training. Use GPUs for fine-tuning and training workloads.
• Very large batch inference — At batch sizes of 64+ where GPUs achieve high utilization, the GPU throughput advantage outweighs FPGA efficiency gains.
• Rapid model experimentation — If you change models frequently and need instant availability, GPU providers have broader model catalogs today.

Switching from GPU to FPGA Inference

OpenFPGA uses the same OpenAI-compatible API format as GPU providers. If you currently use OpenAI, Together AI, Groq, Fireworks, or any OpenAI-compatible provider, switching requires changing one line:

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://app.openfpga.ai/api/v1",
    api_key="your-openfpga-key"
)

response = client.chat.completions.create(
    model="llama-3.1-8b-fpga",
    messages=[{"role": "user", "content": "Hello, world"}]
)
print(response.choices[0].message.content)

JavaScript (fetch)

const response = await fetch("https://app.openfpga.ai/api/v1/chat/completions", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "Authorization": "Bearer your-openfpga-key"
  },
  body: JSON.stringify({
    model: "llama-3.1-8b-fpga",
    messages: [{ role: "user", content: "Hello, world" }]
  })
});
const data = await response.json();
console.log(data.choices[0].message.content);