Qwen 2.5-27B is showing erratic behavior during local testing via Ollama

I have been attempting to run Qwen 2.5-27B through Ollama to verify if it meets the benchmark claims, but the results have been inconsistent. While the 27B parameter count is theoretically the sweet spot for high-end consumer GPUs to achieve near-70B performance without extreme VRAM overhead, I am encountering bizarre inconsistencies in complex reasoning compared to official benchmarks.

Output quality degrades as context expands

The primary problem is not a hard crash, but a significant degradation in output quality as the context window expands. Once a certain token threshold is reached, the model begins looping or hallucinating basic facts that were previously handled correctly in shorter prompts. I suspected quantization was the cause and tested different Q-levels, but the issue remains.

While debugging this performance dip, I encountered a recurring CUDA memory error in my logs during heavy inference:

CUDA out-of-memory error on 24GB VRAM

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 am running a 3090, so I initially assumed I simply lacked enough headroom. However, a deep dive into my AI workflow revealed that the KV cache is bloating much faster than expected for a 27B model. It appears context management in this specific build consumes VRAM rapidly, forcing the system to swap or throttle and resulting in those degraded answers.

Prompt engineering to reduce memory pressure

To address this, I have been experimenting with prompt engineering to constrain output and reduce memory pressure. I also adjusted the num_ctx parameter in the Modelfile to see if capping the context would stabilize the reasoning.

My current diagnostic steps

Quantization trade-offs between speed and logic

1. Quantization Check: I tested 4-bit and 8-bit versions. The 4-bit version is faster, but logic leaps occur more frequently.
2. VRAM Monitoring: Using nvidia-smi in a loop, I tracked memory spikes. They occur exactly when the model generates long-form code blocks.
3. Context Capping: I reduced the context window to 4096. This eliminated the looping bug, suggesting a memory management issue rather than a weight problem.

I remain unconvinced that real-world performance matches the benchmark hype if stability is this unreliable on a 24GB card. If anyone has a practical tutorial for optimizing Ollama configurations for larger Qwen models, please share it.