An open notebook between organic branches and a luminous computational grid.
Open weights, private infrastructure, and on-device execution overlap. They are not synonyms.

§1Name the category before the benchmark

CategoryWhat it meansWhat it does not promise
On-deviceRuns on the laptop or phone in front of you.Frontier capability or long context.
PrivateRuns inside infrastructure you control.One-machine simplicity.
Open weightsThe model weights can be inspected or deployed.Affordable local inference or identical provider behaviour.

§2Current experiment: oMLX + Qwen3.8-27B derivatives

The current stack is oMLX serving two 4-bit Qwen3.8-27B derivatives on my 128 GB M4 Max—one with native multi-token prediction, one drafted by DFlash 2. The download-era status is settled: the measured decode rates, acceptance ranges, and the adopt decision are published in deep dive 37. The bounded jobs stand: private second review, code explanation, contradiction finding, long-document recall, and visual evidence checks. The original receipt explains what “Opus 4.6-class” does—and does not—mean.

FIELD NOTE · 25 AUG 2026

Runner
oMLX 0.6.3rc1
Model
Qwen3.8-27B derivatives · 4-bit · MTP and DFlash 2 lanes
Machine
M4 Max · 128 GB unified memory
Job
Second review and bounded private analysis
Success test
Find a material contradiction and point to the exact passage
Stop
No unsupported rewrite after one focused revision
Status
Measured — best decode 47.2 tok/s; adopted (deep dive 37)

§3Advertised context is not usable context

The useful number is the largest context that preserves recall, instruction following, and output quality on the actual task. Field notes therefore record four separate limits: the configured window, what fits in memory, what completes at acceptable latency, and what remains reliable under retrieval questions.

§4Compare jobs, not personalities

§5Field-note contract

DATE / HARDWARE / RUNNER
MODEL + QUANTIZATION
CONFIGURED CONTEXT
TASK AND INPUT SHAPE
LATENCY / MEMORY
PASS CONDITION
OBSERVED FAILURE
CLOUD COMPARISON
DECISION: ADOPT / TEST / DROP

Current verdict

  • Use local models for bounded jobs with measurable pass conditions.
  • Record exact test conditions before comparing results.
  • Do not equate open weights with one-machine inference.
  • Keep model claims dated; the stack changes faster than the method.