Why is Qwen 2.5-27B acting so erratic on my local setup?

I've been trying to run the Qwen 2.5-27B model via Ollama to see if it actually hits those benchmark numbers people are raving about, but my experience has been a total mixed bag. On paper, the 27B parameter count is supposed to be the "sweet spot" for high-end consumer GPUs, offering near-70B performance without the massive VRAM overhead. However, I'm seeing some bizarre inconsistency in how it handles complex reasoning tasks compared to the official benchmarks.

The main issue I'm hitting isn't a hard crash, but rather a massive degradation in output quality when I push the context window. I started noticing that once I hit a certain token threshold, the model begins to loop or hallucinate basic facts that it handled perfectly in shorter prompts. I suspected a quantization issue, so I tried switching between different Q-levels, but the behavior persisted.

When I tried to debug the performance dip, I ran into a weird CUDA memory error that kept popping up in my logs during heavy inference loads. It looked something like this:

CUDA error: out of memory. Allocated: 22.4GB, Free: 1.2GB, Total: 24GB. 
Attempting to allocate 2.1GB for tensor operation... 
RuntimeError: CUDA out of memory. Tried to allocate 2.1GB (GPU 0); 
total capacity 24GB, already allocated 22.4GB.

I initially thought I just didn't have enough headroom, but I'm running a 3090. I did a deep dive into my AI workflow and realized that the KV cache was bloating way faster than expected for a 27B model. It seems like the way the context is being managed in this specific build is eating up VRAM, which then forces the system to swap or throttle, leading to those degraded "benchmark-defying" answers.

To try and fix this, I've been messing with the prompt engineering to see if I can constrain the output and reduce the memory pressure. I also tried adjusting the num_ctx parameter in the Modelfile to see if capping the context would stabilize the reasoning.

My current diagnostic steps

1. Quantization Check: I tested the 4-bit and 8-bit versions. The 4-bit version is faster, obviously, but the logic leaps are more frequent.
2. VRAM Monitoring: I used nvidia-smi in a loop to track exactly when the memory spikes. It happens exactly when the model starts generating long-form code blocks.
3. Context Capping: I reduced the context window to 4096 to see if the "looping" bug disappeared. It did, which tells me this is likely a memory management issue rather than a weight problem.

I'm still not convinced the real-world performance matches the benchmark hype if the stability is this shaky on a 24GB card. If anyone has a practical tutorial on optimizing the Ollama config for Qwen's larger models, I'm all ears.