On-Chip LLM KV260 Technical Guide

Practical Overview & Architecture

The GitNeural On-Chip LLM KV260 project implements an FPGA-based language model that stores its resident INT4 weight image directly within on-chip memory. By avoiding the 4 GB available off-chip DDR shared controller, the system bypasses the bandwidth wall that traditionally limits single-stream token generation to memory speeds. Operating entirely inside the reconfigurable fabric of the $250 AMD Kria KV260 development board, the architecture achieves a measured performance of 59,965 tokens per second for parallel degenerate streams and over 21,300 tokens per second for a faithful single stream under live testing.

Single-stream token generation is fundamentally memory-bound rather than compute-bound. To produce each subsequent token, the system must read every model weight once. When weights reside in off-chip DRAM, the Arm cores and the hardware fabric compete for the same limited ~20 GB/s bandwidth controller. This implementation escapes that limitation by scaling the model down to fit within approximately 3 MB of on-chip resources, leveraging UltraRAM for weights and Block RAM for activations, scratch space, and the KV cache.

Prerequisites & Hardware Setup

Deploying this configuration requires specific physical hardware, notably the AMD Kria KV260 Vision AI Starter Kit featuring the K26 System-on-Module. Development relies heavily on a robust host environment, specified in documentation as a 20-core Intel i7 workstation equipped with 32 GB of RAM to handle design synthesis, placement, and routing. A spare SD card flashed with a fresh Linux image is required for board deployment, while remote management tools such as SSH and JTAG access streamline iteration workflows.

Software prerequisites avoid High-Level Synthesis tools entirely, relying instead on manual Register Transfer Level implementations written in Verilog. Rather than launching graphical design suites like Vivado interactively, the documented build workflow executes synthesis and bitstream generation entirely through the command line interface. Host configuration also accommodates serving infrastructure components, including a Cloudflare tunnel and local proxy processes to bridge incoming WebSocket connections from clients directly into the FPGA fabric.

Documented Implementation Workflow

The development lifecycle centres around command-line automation for compiling hardware descriptions into executable bitstreams. Because the project completely bypasses HLS compilation from C or C++, engineers interact directly with Verilog modules and helper scripts stored in the public repository github.com/MichaelAyles/kev-gpt. A complete compilation cycle on the reference 20-core i7 workstation takes approximately thirty minutes, with execution times increasing as place-and-route algorithms approach maximum on-chip memory utilization limits.

Training corpus preparation forms an optional preprocessing step to maximize effective throughput. By running input text through the Keviniser tool, training data is stripped down to content words, achieving a corpus compression of approximately seventy percent. While this compression does not alter the underlying parameter count or physical memory footprint of the model, it reduces the character length required to express stories, effectively boosting perceived generation speed by a factor of roughly 1.5x.

Known Limitations, Tradeoffs & Error Scenarios

The core architectural advantage relies entirely on keeping the entire model resident in on-chip SRAM, which introduces a hard scaling crossover point at approximately 6.3M parameters. Pushing model sizes or context windows beyond this strict on-chip capacity forces memory spills back out to DDR, immediately sacrificing the speed advantage. Furthermore, attempts to pack additional multiply-accumulate operations into the device's DSP48E2 blocks reveal a hard silicon ceiling, as larger configurations demand more hardware resources than the underlying Zynq UltraScale+ chip physically possesses.

Usability tradeoffs accompany extreme optimization efforts. Highly parallelized configurations operating with a restricted attention window of one token deliver raw aggregate metrics exceeding 59,000 tokens per second, but produce degenerate, repetitive text output that lacks contextual memory. Conversely, maintaining a faithful single stream with full on-chip KV caching reduces throughput to roughly 19,242 counted cycles per second while ensuring coherent multi-turn conversation capabilities under real-world connection loads.

Who Should Use It & Production Fit

This engineering approach targets hardware developers, embedded systems researchers, and FPGA enthusiasts exploring extreme hardware-software co-design. It serves as an exceptional educational instrument and measurement platform for investigating memory-bound inference bottlenecks outside of conventional GPU accelerators. Professionals studying low-level digital logic design and custom RTL optimization will find valuable reference implementations within the open-source repository.

Production deployment is strictly constrained by the severe capacity limitations of low-cost on-chip memory budgets. Organizations requiring general-purpose conversational agents, robust natural language understanding, or complex reasoning capabilities should avoid this toy-model technique. However, teams prototyping ultra-low-latency edge applications where predictable, deterministic response times outweigh semantic complexity can adapt these hardware sequencing principles for specialized on-chip inference tasks.