A Reddit user, Future_Fuel_8425, reports losing a bank of 8GB DDR4 ECC after getting to work Ornith-1.5-35B-A3Ba 35 billion parameter model, on complex database tasks. The system was crashing when he returned, about 90 minutes after startup, and 8-9 days later a second desk started reporting errors. The temperatures of the CPU and GPU appear in the story, never above 80°Cbut no memory reading.
Before the change the user used a 20B model, which was completely in 16GB of GDDR7 from the RTX 5070 Tibut found it too limiting for daily work. The 35B offered him superior quality, and to run it he downloaded part of the model to system memory: 128GB DDR4 ETC taken from a workstation, in 8GB banks. According to him, the token generation speed does not decrease when the VRAM runs out and the system RAM takes its place, and this is one of the reasons why he preferred this configuration.
Heavy local LLM use kills old ram
by u/Future_Fuel_8425 in LocalLLM
After returning home about 90 minutes later, the user found the system blocked and, upon investigation, discovered that one of the 8GB banks was no longer working properly. The time indicated is the time of return and the story does not say when the desk stopped working. The user removed the module and relaunched the same model, thinking of a faulty bench, an eventuality which according to him happens all too often. After 8-9 days checking the error logs showed a second bench on the verge of giving way.
The user excludes that the heat had a role, but as a reference he only cites the CPU and GPU, which never exceeded 80°C. The temperatures of the memory modules are missing, and the story says nothing about air exchange in the case. The desks came from a workstation, and their service age is not indicated.
The hypothesis is that the heat killed the first bank and rapidly degraded the second, with module age, prolonged stress and insufficient cooling the most likely combination.
There is also the possibility, since these are “recovery” modules, that they were in reality already affected by some type of error which does not manifest itself in conditions of normal use, but which quickly surfaces when the memory is used massively as is the case of an LLM use with offload on the system RAM.
In the Reddit thread other users report having had similar experiences, confirming that completing the LLM is a particularly burdensome workload for the memoira. Without the full picture, however, it is difficult to establish the real cause, but the episode serves as a warning: if you run language models locally, especially in the case of intense and prolonged workloads, take care to use “healthy” and properly cooled memory modules.
