ROCmForge: A Large Language Model Inference Engine Built Exclusively for AMD GPUs

ROCmForge is an open-source inference engine that enables AMD GPU users to run large language models efficiently locally, breaking the monopoly of the CUDA ecosystem.

• Original Author/Maintainer: oldnordic
• Source Platform: GitHub
• Original Title: ROCmForge
• Original Link: https://github.com/oldnordic/ROCmForge
• Publication Date: 2026-06-11

In the field of local deployment of large language models (LLMs), NVIDIA's CUDA ecosystem has long dominated. Most open-source inference frameworks like vLLM and TensorRT-LLM prioritize or even only support CUDA, putting users with AMD GPUs in an awkward position. Although AMD has launched ROCm as an open-source alternative, the maturity of its software ecosystem still lags behind, especially in LLM inference optimization.

The emergence of ROCmForge is precisely to fill this gap—it is an LLM inference engine designed specifically for AMD GPUs, aiming to allow users of Radeon and Instinct series GPUs to enjoy an efficient, low-latency local AI experience.

ROCmForge is a lightweight yet fully functional inference engine focused on achieving optimal LLM inference performance on AMD hardware. Unlike general cross-platform solutions, ROCmForge has been deeply optimized for AMD's CDNA and RDNA architectures from the start, making full use of the features of the ROCm software stack.

The core goals of the project include:

1. Native AMD Support: Built on ROCm/HIP, no CUDA compatibility layer required
2. Efficient Memory Management: Optimized KV cache strategy for AMD GPU memory architecture
3. Multi-Quantization Support: Built-in parsing for formats like GGUF, GPTQ, AWQ to reduce memory usage
4. Streaming Generation: Supports token streaming output to improve interactive response speed
5. OpenAI-Compatible API: Provides an HTTP interface compatible with the OpenAI API for easy integration

ROCmForge is built on AMD's ROCm (Radeon Open Compute) platform and uses HIP (Heterogeneous-compute Interface for Portability) as the programming interface. HIP allows developers to write code that runs on both AMD and NVIDIA GPUs, but ROCmForge is specifically tuned for the memory hierarchy and compute unit layout of AMD hardware.

AMD GPUs have significant differences in memory architecture compared to NVIDIA. ROCmForge adopts the following targeted optimizations:

• Hierarchical KV Cache: Designed a hierarchical cache strategy based on the HBM2/HBM3 characteristics of AMD memory, keeping active KV pairs in high-speed memory areas
• Paged Attention: Implements the PagedAttention mechanism to support efficient processing of long contexts
• Dynamic Batching: Dynamically adjusts batch size based on memory pressure and computational load

The project has been specifically optimized for the matrix compute units (Matrix Core) of AMD's CDNA architecture:

• MFMA Instruction Utilization: Makes full use of AMD's matrix fused multiply-add instructions to accelerate attention computation
• Wavefront Scheduling Optimization: Optimizes thread layout for AMD's 64-thread wavefronts
• Asynchronous Data Transfer: Overlaps computation and data transfer to hide memory latency

ROCmForge is suitable for the following user groups:

Individual Developers and Researchers

Users with consumer-grade GPUs like the Radeon RX 7900 XTX can finally run models at the 70B parameter level locally. Taking the RX 7900 XTX's 24GB memory as an example, open-source large models like Llama-2-70B or Mixtral-8x7B can run smoothly with 4-bit quantization.

Enterprise Data Centers

For data centers deploying AMD Instinct MI series accelerators, ROCmForge provides a more cost-effective inference solution. Compared to the high price of NVIDIA A100/H100, the MI210/MI250 series combined with ROCmForge can offer competitive cost-performance in certain scenarios.

Privacy-Sensitive Scenarios

Like all local inference solutions, ROCmForge ensures data does not leave the local machine, making it suitable for application scenarios handling sensitive information, such as internal document analysis in medical, financial, and legal fields.