Technical Development in LLM Inference

A new software implementation, gemma4.c, has been released on GitHub, aiming to provide a transparent, low-level approach to Large Language Model (LLM) inference. Developed by Ryan Senn, the project focuses on implementing the Gemma 4 E2B model—one of Google’s recent open models—using a single C source file consisting of approximately 700 lines of code.

According to the developer, the primary objective of the project is to move beyond abstract equations and diagrams, allowing users to trace the entire inference process—from the initial prompt to token generation—directly through the source code. The implementation handles the tokenizer, transformer architecture, KV cache, sampling, and CPU kernels without the use of external inference frameworks or libraries.

Performance Benchmarks on Consumer Hardware

The implementation is specifically optimized for CPU-based inference. The runtime utilizes int8 weights and activations, leveraging OpenMP and hardware-specific instructions such as AVX2 and AVX-512 VNNI where available.

In unverified performance tests conducted on a Ryzen 7 7700 processor, the developer reported the following benchmarks:

  • Prefill speed: Approximately 639 tokens per second (tok/s) on a 512-token sequence.
  • Generation speed: Approximately 25.9 tok/s.

The developer claims these figures exceed the performance of llama.cpp under similar conditions. While these metrics suggest high efficiency for a lightweight implementation, they have not been independently verified by third-party benchmarking firms.

Community Reception and Technical Scope

Initial feedback from the developer community on Reddit has been mixed regarding terminology and scope. While some users noted the impressive speeds achieved on CPU hardware, others suggested that “inference engine” or “runtime” would be more accurate descriptors than “implementation.”

The project maintains a limited scope by design. It is built exclusively to support the Gemma 4 E2B model and CPU inference, avoiding the complexity of a general-purpose runtime. This streamlined approach is intended to keep the codebase manageable and readable for those seeking to understand the mathematical operations and buffer allocations involved in transforming activations into new tokens.