User reports a crystallization experiment on Qwen2.5-7B-Instruct using an activation-steering engine called AkbasCore 3.0 / SEASC on 1× NVIDIA A100-SXM4-40GB. Setup uses LoRA gradient updates to transfer part of the induced layerwise representation geometry into the model weights, then removes the runtime steering motor and adapter. Training ran 1.549 min (~93 seconds) and consumed 2.6329 Wh of measured GPU-board energy at a time-weighted mean board power of 102.33 W. The locked final test showed +15.7881% progress with the motor off, best state at update 40, and automatic overshoot detection at update 70. An unresolved merge diagnostic reports 1.762% relative hidden-state drift.
NVIDIA A100 40GB reports
2 reports. These are individual setups, not a controlled benchmark. NVIDIA A100 40GB speeds and models →
- generation:
- 16.1 tokens/s
- quant:
- Q8_K_XL (GGUF)
Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.
User reports all experts on CPU with only 15.8 GB of 40 GB VRAM used.