Deploying open models locally or on edge instances requires balancing memory usage against baseline output coherence. While marketing charts highlight smooth four-bit quantization support, the actual perplexity loss depends heavily on how weight distribution was handled during initial training.
Quantization Methods Compared in Practice
We evaluated standard AWQ and GPTQ four-bit quantization passes against raw sixteen-bit baseline checkpoints across several coding benchmarks. While general prose generation remains stable, precise code syntax and mathematical reasoning degrade non-linearly at lower precision levels.
Small parameter models suffer significantly more performance degradation under four-bit precision than seventy-billion parameter variants. The larger capacity allows bigger models to retain logical coherence even when precision is aggressively compressed.
Measuring Latency and Memory Throughput
Memory bandwidth remains the fundamental bottleneck for local inference speed on standard workstation hardware. Shrinking model weights to fit entirely into unified high-speed VRAM yields a much higher token generation rate than running larger unquantized models across system RAM channels.
Choosing the Right Precision Tier
For local development and inline code completion, six-bit quantization offers the optimal balance between memory efficiency and output fidelity. Reserve four-bit quantization strictly for search summarization and casual conversational tasks where syntax precision is secondary.