Show HN: I ran a language model on a PS2

Posted by xaskasdf 3 days ago

Show HN: I ran a language model on a PS2(github.com)

The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc.

Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this.

And it works. On real hardware.

25 points | 8 comments

randkyp 2 hours ago|

Neat! While the physicality of having the CD spin while running inference is undeniably cool, I wonder if you could run larger models at higher speeds through the PS2 HDD accessory/Memory Card Micro SD adapter/the PS2's USB port.

I doubt the VUs can help with inference given their small scratchpad sizes and instruction set though, haha.

accrual 19 minutes ago|

The PS2's USB port is limited to 1.1 speeds so unfortunately it's much slower than the CD interface. The phat models have an internal IDE port that is trivally converted to SATA though, and is plenty fast with an SSD!

pooparse 2 hours ago||

IIRC the EE had some interesting hardware with vector units. Were these of any use/benefit here?

mememememememo 3 days ago||

How many tok/hr?

SilentEditor 3 days ago||

Love this project. The CD streaming trick is such a smart constraint hack, and honestly the best part is you trained the model for the hardware instead of forcing a desktop recipe onto PS2.

Curious about 2 things if you can share:

whats your per-token latency on real hardware how much quality loss came from PSNT quantization vs fp16 baseline Either way this is peak hacker energy, shipping on actual hardware makes it 10x cooler.

xaskasdf 3 days ago|

It didn't had any quality loss, since the PSNT as quantization it's mainly to convert the model over the console constraints (you can convert any model you want, even when i trained a model for this hw); it's q8 quantization, so quality loss is negligible for these sizes. For the speed, I will fix the Tok/sec count since now drops 0 always for showing measures

PS: Thank you! And forgot to mention PSNT also supports bitnet models, they work like crap tho

SilentEditor 1 day ago||

Thats super helpful, thanks for the details. Makes sense now that PSNT is more of a transport/runtime format for the PS2 constraints than a quality hack.

Very cool that it supports bitnet too even if results are rough right now, feels like theres a lot of room to tune there over time. when you do fix tok/sec, are you planning to post per-stage timings too (tokenizer, weight stream, matmul, samppling)? would be awesome to see where the biggest bottleneck is on real hw

SachitRafa 2 days ago|

The CD-ROM streaming approach is the real insight here, keeping only activations and KV cache in RAM and streaming weights one matrix at a time sidesteps the 32MB constraint entirely. It's essentially the same trick modern edge inference does with flash storage, just on hardware from 2000. Curious about the latency profile, with CD-ROM read speeds around 1.6 MB/s on PS2, the 77MB SmolLM2 model being too slow makes sense, but how does the 10MB brandon-tiny feel in practice? Are you getting tokens per minute or more like tokens per several seconds? Also interested in the custom PSNT format decision, was the main motivation the PS2's MIPS alignment constraints, or was there something about the existing GGUF/llama.c formats that made them impractical to parse on the Emotion Engine?