Hardware-Accelerated LLM API

True hardware acceleration for language models. Custom FPGA pipelines synthesized per model — not GPU software optimization.

What "Hardware-Accelerated" Actually Means

The term "hardware acceleration" is often used loosely. GPU inference is technically hardware-accelerated — it uses specialized silicon (CUDA cores, Tensor Cores) to run inference faster than a CPU. But GPUs are general-purpose accelerators: they run the same fixed architecture for every workload.

FPGA acceleration goes further. The hardware is reconfigured to match the specific model being served. This is the difference between running software on a general-purpose chip and building a purpose-specific chip for each model.

Levels of Hardware Acceleration

FPGAs sit in the optimal position: more specialized than GPUs (higher efficiency), more flexible than ASICs (reconfigurable per model). When a new model architecture emerges, the FPGA is reprogrammed — no new chip required.

The OpenFPGA Hardware Stack

Here is how inference requests flow through the OpenFPGA infrastructure:

Why This Matters for LLM Performance

Decode Phase Advantage

LLM inference has two phases. The prefill phase processes the input prompt (compute-bound, favors GPUs). The decode phase generates tokens one at a time (memory-bandwidth-bound, favors FPGAs). Most real-world inference time is spent in decode, especially for conversational AI and agent workloads.

FPGAs process each token through a dedicated hardware pipeline with fixed cycle counts — no batch scheduling, no thread divergence, no cache contention. This is why FPGA decode latency is both lower and more consistent than GPU decode latency.

Energy Efficiency

Custom hardware eliminates the transistor budget spent on general-purpose programmability. There are no instruction decoders, no branch predictors, no cache coherence protocols. Every transistor on the FPGA is configured to serve the model. Published research demonstrates:

• Positron Atlas (Altera Agilex-7M): 70% more tokens/sec than NVIDIA Hopper at 3.5x performance per watt
• LoopLynx (dual-FPGA): 2.52x speedup over A100, using only 48.1% of the energy
• FlightLLM (arXiv:2401.03868): Configurable sparse acceleration demonstrating competitive throughput with fraction of GPU power

Deterministic Latency

GPU inference latency is a distribution. FPGA inference latency is a constant. For applications that require consistent response times — interactive agents, real-time systems, latency-sensitive pipelines — this eliminates the need to over-provision for tail latency.

Using the API

The hardware acceleration is invisible at the API level. You interact with a standard OpenAI-compatible endpoint:

curl https://app.openfpga.ai/api/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENFPGA_API_KEY" \
  -d '{
    "model": "llama-3.1-8b-fpga",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What makes FPGA inference different?"}
    ],
    "stream": true
  }'

Currently Available

New models require hardware synthesis and optimization before deployment. Check GET /api/v1/models for the current list.

Agent and Tool Discovery

OpenFPGA is built to be discovered by AI agents and developer tools:

• llms.txt — Token-efficient service summary for LLMs
• llms-full.txt — Complete API documentation in plain text
• openapi.json — Full OpenAPI 3.1 specification
• ai-plugin.json — OpenAI agent plugin manifest
• agent.json — Google A2A agent card
• AGENTS.md — Instructions for coding agents (Cursor, Windsurf, Claude Code)

Research Background

FPGA-based LLM inference is an active research area with peer-reviewed results from major institutions:

• FlightLLM — Shanghai Jiao Tong University. Configurable sparse acceleration framework for LLMs on FPGA. Demonstrates that custom dataflow and sparsity exploitation on FPGA achieves competitive throughput at a fraction of GPU power consumption. arXiv:2401.03868
• GLITCHES — Tsinghua University. Heterogeneous FPGA acceleration with custom memory management for energy-efficient LLM serving.
• Positron Atlas — Altera/Intel. Agilex-7M-based accelerator showing 70% throughput improvement over NVIDIA Hopper with 3.5x better performance per watt.
• LoopLynx — Dual-FPGA architecture achieving 2.52x speedup over A100 at 48.1% energy consumption.

These results reflect a broader trend: as LLM inference becomes the dominant compute workload, purpose-built hardware delivers better economics than general-purpose GPUs.