Comment by jjcm
10 hours ago
I'm still sad that we haven't seen a new Taalas style chip a la https://chatjimmy.ai/. Smaller models are good enough now to make that insane burst of tokens so useful.
10 hours ago
I'm still sad that we haven't seen a new Taalas style chip a la https://chatjimmy.ai/. Smaller models are good enough now to make that insane burst of tokens so useful.
I don't know the model behind this, but it is absurdly bad.
> Write me a coherent paragraph in French, without ever using the letter "e".
> Voilà une phrase claire et concise : "Le village est situé dans les montagnes. Le soleil est haut. Il y a des animaux dans le village. Il pleut dans les montagnes."
I suppose this is just a demo of how fast an LLM can be, I wonder if there are tradeoffs with larger/smarter models. Also, for a human usage, at what point are tokens generated fast enough that it's pretty much instant? My bet is below 1000 tps
Why ask this when we know that LLMs are not good at the character level. They run on tokens, not characters. In fact, they don't even see the characters, unless you do special tricks.
I asked it to translate your sentence to English and it did fine.
To be fair, you picked a well-known tricky benchmark for LLMs: When working on an embedding spelling disappears after the embedding level. I imagine modern frontier models have tools that let them read back their input to work around this issue.
Its Llama 3.1 8B, a very old/small model.
That's the problem with etching a model onto a chip: by the time you've designed the chip, manufactured it, tested it, shipped it, and deployed it, the model will be hopelessly outdated (with the current improvement rates). And when you want to update, you have to buy new chips instead of just uploading a new model file like now. When Taalas announced their chip, the model was already 1.5 years old (stone age by current standards). It's their first chip, so maybe they can streamline it, but the problem of having to update hardware every few months to keep up with the industry is not going anywhere.
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Wasn't it also quantized aggressively, like 1 or 2 bits?
for comparison, Qwen3.8-Flash-Next only requires 6B parameters for computation, but stores 125B, 51B of those can be comfortably offloaded as they're not actively used in decode but a single token look up.
The Quant iQ4 of this model loads, then, in ~60GB of vram, and on disk it's 85GB.
So if you could etch it, you'd need a ~25GB ssd chip and 60GB of vram.
The vram costs likely contributed to these things being out of reach of the current economic cycle.
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Then again, good luck writing a coherent paragraph in French without an "e". :-)
There is a book written under this premise. Probably the inspiration for that prompt
https://en.wikipedia.org/wiki/A_Void
Pretty sure context is in SRAM, and then you have that as a blocker for tasks.
way less of a blocker these days due to sparse attention...