Llama.cpp on a jailbroken PS5: run any GGUF model natively on the console
A jailbroken PlayStation 5 can natively run any GGUF model using Llama.cpp, turning the console into a language model server for all network devices. Performance varies by model size; for instance, a PS5 Slim can process Qwen 3.5 0.8B at 207 tokens/second, Qwen 3.5 9B at 55 tokens/second, and Qwen 3.8 27B at approximately 20 tokens/second with a 64k context. A d1 Omni 600M decision model can answer 3 questions in 0.3 seconds.
This report details the first instance of a jailbroken PS5 natively running GGUF models via Llama.cpp, unlike previous methods that required more specialized hardware.
时间与来源
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发布当时偏移：UTC+02026年10月16日 18:32 UTC
收录当时偏移：UTC+02026年10月16日 19:00 UTC
- 发布
- 2026年10月16日 18:32
- 收录
- 2026年10月16日 19:00
- 来源类型
- 开发者社区
- 档位
- 社区
- 信源状态
- 正常
档位是按信源手工设定的编辑判断，不是逐条打分。
- 判定依据
- 热度约为该来源近期上榜条目中位水平的 2.4 倍
- 指标对比
- 215 vs 中位 88（20 条基线样本）
- 检出时间
- 10/17 03:00
Your PS5 runs the language model. Every device on your network uses it.
llama.cpp on the console's GPU, with OpenAI and Anthropic APIs and a screen made for the TV.
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New: decision models, powered by Ollaya. PS5LM now runs Jev-like decision models (d1, Clef, OpenJev and more) on the GPU and answers the same /v1/systemone API as Ollaya: typed decisions with calibrated probabilities, in a fraction of a second.
PS5LM is for anyone with a jailbroken PS5 who wants a private model server at home. It loads a GGUF model onto the console's GPU and serves it to your laptop, your phone and your coding tools. It needs no PC while it runs, no cloud and no account.
Qwen 3.5 9B answering requests from a laptop on a PS5 Slim. Click for the video.
Home, Models, Console, API and Settings on the TV.
A model server under your TV
- It runs on the GPU. llama.cpp's Vulkan backend on the console's RDNA 2 GPU, with 11.4 GB of the shared memory for the model and its context. Every architecture and quantization llama.cpp knows comes along.
- It answers your network. OpenAI's /v1/chat/completions, Anthropic's /v1/messages (Claude Code works against it) and a chat page in any browser. Point a client at http:// :8081 and use any model name: the loaded one answers.
- It decides, too. Load a decision model and the console answers TypeSafe's /v1/systemone: choice, score and yes-or-no questions about text and images, the API Ollaya serves on a PC.
- It shows its work. Every request streams across the TV as a chat: which device asked, what it asked, and the reply as it is written. Square sends a demo and Triangle opens a keyboard, so the console can be tried from the couch.
- It watches the console. A Console page charts temperature, power, GPU and CPU load over the last two minutes or the last hour, beside a table of every reading.
- It picks what fits. The planner reads each GGUF and gives it the longest context and best cache that fit the memory free. A Suggested list holds models measured on the console, and the app downloads from Hugging Face by itself, to internal storage, the M.2 drive or a USB drive.
- It reads images. Models with an image projector (Gemma 4, MiniCPM-V, d1) take pictures in chat and in decisions.
Model Size On a PS5 Slim
Qwen 3.5 0.8B, Q4_K_M 0.5 GB 207 tokens a second
Qwen 3.5 9B, Q4_K_M 5.3 GB 55 tokens a second
Qwen 3.8 27B, UD-Q2_K_XL 9.2 GB about 20 tokens a second, 64k context
d1 Omni 600M, a decision model 1.1 GB 3 questions in 0.3 s; a 720p image and 7 questions in 2.9 s
Decision models, powered by Ollaya
Ollaya runs open decision models on a PC the way Ollama runs language models. PS5LM brings the same idea to the console: a decision model reads a state and answers typed questions in one forward pass, with no text generated.
curl http:// console > :8081/v1/systemone -H ' Content-Type: application/json ' -d ' { "state": "I was charged twice this month. Refund me or I cancel.", "questions": { "intent": {"type": "choice", "instructions": "What does the customer want?", "criteria": {"refund": null, "information": null, "cancel": null}}, "angry": {"type": "noul", "instructions": "Is the customer angry?"} } } '
The Suggested list has d1 Omni 600M, d1 3B and Clef Flash. Any GGUF that carries llama.cpp's decision metadata loads the same way, and the app labels it a decision model.