Gemma 4B beats Mistral 7B for batch message grading on limited hardware

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A developer testing LLM-based message grading on an RTX 4050 GPU with 6GB VRAM found that hardware constraints heavily influenced model selection. Initial trials with phi-4-mini failed at batch grading, producing schema-compliant output only 60% of the time and adding over 10 minutes of latency per 130 messages. Mistral 7B passed early tests but broke down on real-world mixed-length messages because its 8K context window was insufficient for average token loads of around 16K. Gemma 4B, despite having fewer parameters, offered a 32K context window that comfortably handled peak usage of 28K tokens while maintaining full schema compliance across test batches. The choice highlighted that optimal model selection under hardware constraints prioritizes fit-for-purpose performance over raw model capability.

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