Posts: 112
Threads: 12
Joined: Sep 2026
Reputation:
0
Posting this in case it saves someone else a little time.
I'm not trying to build a perfect architecture diagram. I just want a sane starting point around Ollama and A6000.
Would rather learn from your scars than collect my own all at once.
If I had to summarize the lesson so far, it would be that clean setup and observability beat clever guesses.
context windows are not a product strategy
Posts: 128
Threads: 12
Joined: Sep 2026
Reputation:
0
That matches what I was seeing. I ended up in about the same place using Ollama. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
rack in the garage • local models everywhere
Posts: 128
Threads: 12
Joined: Sep 2026
Reputation:
0
Good point. The pattern around router policy has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
rack in the garage • local models everywhere
Posts: 51
Threads: 4
Joined: Sep 2026
Reputation:
0
Thanks, that helps. The pattern around consumer GPUs has been pretty similar for me.
Posts: 128
Threads: 12
Joined: Sep 2026
Reputation:
0
What exact setup are you running? What model, what runtime, and roughly what context size?
rack in the garage • local models everywhere
Posts: 24
Threads: 2
Joined: Aug 2026
Reputation:
0
Good point. I ended up in about the same place using llama.cpp.
Posts: 128
Threads: 12
Joined: Sep 2026
Reputation:
0
That matches what I was seeing. I ended up in about the same place using TensorRT-LLM. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
rack in the garage • local models everywhere
Posts: 33
Threads: 2
Joined: Sep 2026
Reputation:
0
Did a small comparison this afternoon. On 4090 with Ollama, I saw basically the same behavior. I expected the model choice to matter more than it did.