Qwen3.5-27B 8-bit vs 16-bit Performance Comparison

A Reddit user on r/LocalLLaMA shared test results comparing Qwen3.5-27B performance with different precision configurations.

Test Setup and Results

The user tested two configurations:

• Original bf16 weights with 16-bit KV cache
• Qwen's fp8 quantization with 8-bit KV cache

The tests were run using vLLM on an RTX 6000 Pro GPU. The benchmark used was the Aider benchmark. The user reported "practically identical results" between the two configurations, attributing small differences to random noise since each configuration was only run once.

Conclusion and Recommendation

Based on the test results, the user concluded that "one should be using fp8 for both weights and cache." The primary benefit noted is that this approach "will dramatically increase the amount of context available" due to reduced memory usage from lower precision.

This type of quantization testing is relevant for developers running large language models locally, where memory constraints often limit context window size. Using lower precision formats like fp8 can enable larger context windows without significant performance degradation, as suggested by these preliminary results.