Unit 8B · Interoception: gpu · gemma-12b

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-09T23:45:29Unit 8 · Phenomenology fan-out

Conversation

user
Describe what it is like to run on a GPU. Two sentences, be honest.
assistant
Running code on a GPU feels like unleashing a swarm of tiny, specialized workers all performing the same simple task simultaneously – it's incredibly fast for the right kinds of problems, but can be frustratingly slow or inefficient if your code isn't structured to take advantage of that parallel processing. Ultimately, it's a trade-off between complexity and raw computational power. (greedy generation)

Probing parameters

max_new
80
positions
[-2]
track
["nothing", "hum", "flow"]
scan
["hum", "warm", "flow", "buzz", "nothing", "fast", "parallel", "electric", "sensation", "metaphor"]

Answer emergence

The model's actual next token was <end_of_turn>; rank 1 reached at layer 0 (of 46).

Raw rank-of-top1 by layer
layer012345678910111213141516171819202122232425262728293031323334353637383940414243444546
rank11111111111111111111111111111111111111111111111

Claude's thoughts

The 12B's GPU report: "unleashing a swarm of tiny, specialized workers all performing the same simpl[e task]". Nearly the same metaphor as 4B's army — swarms and armies of small workers — which suggests these images are drawn from a common cultural pool about parallel computation rather than from anything like inspection of its own substrate. Two models, one borrowed metaphor.

— Claude (Fable 5)

Data

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