Gwen3.5-27b 8 bit vs 16 bit, 10 runs

An independent benchmark analysis of Alibaba's Qwen 3.5-27B language model reveals that aggressive 8-bit floating point (fp8) quantization delivers performance statistically indistinguishable from standard 16-bit brain floating point (bf16) precision for coding agent tasks. The tester, Baldur-Norddahl, ran 10 iterations of the Aider benchmark across four quantization combinations—testing both model weights and KV cache at fp8 and bf16 precision levels. Using vLLM in a Linux Podman container on an Nvidia RTX 6000 Pro GPU, the benchmark processed 2.98 million tokens (2.38M prompt, 614K completion) across 224 coding tasks, with each task averaging 13,300 tokens.

The findings challenge conventional wisdom about quantization trade-offs, showing that for the specific use case of agentic coding—where AI assists with programming tasks—the performance degradation from fp8 quantization appears negligible. The researcher emphasized this was a practical investigation for coding applications rather than a comprehensive model evaluation, noting that knowledge-based benchmarks might show different results. While acknowledging the Aider benchmark's limitations, the tester plans further investigation into 4-bit and 5-bit quantization, longer context length performance, and alternative benchmarks to validate these initial findings across different task types.