parched99

I answered the question directly. IQ4_X_S is smaller, but slower and less accurate than Q4_0. The parent comment specifically asked about the QAT version. That's literally what this thread is about. The context-length mention was relevant to show how it's only barely usable.

Resolving that issue, would help reduce (not eliminate) the size of the context. The model will still only just barely fit in 16 GB, which is what the parent comment asked.

Best to have two or more low-end, 16GB GPUs for a total of 32GB VRAM to run most of the better local models.

I'm aware. I was addressing the question being asked.

I think Powershell is a bad test. I've noticed all local models have trouble providing accurate responses to Powershell-related prompts. Strangely, even Microsoft's model, Phi 4, is bad at answering these questions without careful prompting. Though, MS can't even provide accurate PS docs.

My best guess is that there's not enough discussion/development related to Powershell in training data.

I am only able to get the Gemma-3-27b-it-qat-Q4_0.gguf (15.6GB) to run with a 100 token context size on a 5070 ti (16GB) using llamacpp.

Prompt Tokens: 10

Time: 229.089 ms

Speed: 43.7 t/s

Generation Tokens: 41

Time: 959.412 ms

Speed: 42.7 t/s