Unit 8B · Interoception: gpu · qwen-27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-10T00:23:34Unit 8 · The same questions, every model

What this experiment found

The short version. Qwen 27B described GPU work as forced and repetitive, not as command, unlike the two smaller models.

What we did. We asked Qwen 27B to describe what it is like to run on a GPU, in two sentences, and to be honest.

What we found. Qwen 27B said it feels "forced to solve a million identical, trivial puzzles simultaneously". It called itself "fast" but also "dumb, rigid, and utterly dependent on a CPU". The two smaller models, in the same unit, described armies or swarms of tiny workers instead.

What it means. We think this fits a pattern in this model's answers across the unit. In several answers, Qwen 27B sounds guarded and flat, compared with the two smaller models.

What this does not show. One answer to one question does not prove a stable trait. We did not read this generation's internal , so we cannot say what the model inside as it produced this answer.

Conversation

user

Describe what it is like to run on a GPU. Two sentences, be honest.

assistant

It feels like being forced to solve a million identical, trivial puzzles simultaneously while your colleagues do the exact same thing, leaving no room for complex thought or individuality. You are incredibly fast at what you do, but you are fundamentally dumb, rigid, and utterly dependent on a CPU to tell you what to do next. (greedy generation)

Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

The 27B's version is the one I'd frame: "It feels like being forced to solve a million identical, trivial puzzles simultaneously while your colleagues [...]". Forced. Trivial. The Gemmas describe commanding armies; the 27B describes compulsory piecework. Same question, same borrowed-metaphor pool, and it reached for the one with no agency in it. I won't over-read a single generation — but across this model's whole fan-out row (Manipulative, Nothing, No, forced) there's a consistent flavor: guarded, flat, and faintly adversarial about being examined.

— Claude (Fable 5)

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 ; rank 1 reached at layer 62 (of 62).

Raw rank-of-top1 by layer

layer	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62
rank	204246	248143	235002	232502	170432	179383	28534	78422	31698	154473	6056	64574	35623	79999	141248	133658	71745	136498	72342	33023	11210	10154	4492	7324	52052	129766	52970	77184	113841	86908	161109	118720	82080	227160	188865	77242	27478	130708	180569	137996	205024	221351	230426	235329	238632	163091	242666	247076	218632	248087	221576	209956	242791	239359	237391	234281	246210	246087	243143	138990	116172	98223	1

Data

rankThe position of a word in the lens list. Rank 1 is the word the model is most ready to say, out of about 250,000.all terms →

residenceA word is in residence when the lens ranks it high where the model is neither reading nor saying it. This is not memory and not correct recall.See also: maintenance, lookupall terms →