Native AI Execution
A developer known as u/AdIllustrious7246 has released a fork of PS5LM, a llama.cpp port designed to run large language models locally on a jailbroken PlayStation 5. The update allows the software to execute natively and leverage the console's GPU, running models such as Qwen 27B at 20 tokens per second.
An unofficial port of llama.cpp was previously introduced by GitHub user cobanov to enable offline AI chat models on hacked hardware. That initial iteration operated as a CPU payload, restricting memory access to roughly 6.25 GiB to prevent the home screen from freezing. While cobanov intended to eventually utilize the GPU, the new fork advanced those capabilities further.
Qwen 27B at ~20 tok/s on a jailbroken PS5's GPU. I forked PS5LM and turned it into a native appfrom r/LocalLLM
Hardware Acceleration
The project relies on PS5_Vulkan, an adaptation of the RADV graphics driver that routes instructions to the AMD-based PS5 GPU. Although the hardware is traditionally locked down against general-purpose computing, Vulkan-based homebrew projects have expanded access to its processing architecture.
Performance Benchmarks
By shifting execution to the GPU and running natively, the modified console processed a 27-parameter model at 20 tokens per second, alongside smaller options like Gemma 4 E4B and Llama 3.2 3B hitting 61 and 124 tokens per second respectively. Previous CPU-bound setups struggled with larger files due to system constraints.
Features and Interface
The native implementation expands usable RAM to 11.4 GiB. It also introduces a dashboard tracking live generation metrics, power draw, temperatures, and memory allocation between the model and the KV cache. Integration with llama.cpp's web user interface enables external browser-based access from other local network devices.
While the project demonstrates the feasibility of repurposing console hardware for AI tasks, widespread adoption for similar workloads remains limited by hardware constraints compared to dedicated enterprise accelerators.
