Quantizing weight matrices remains the primary lever for reducing model footprint and increasing inference throughput. However, choosing between FP8 floating point formats and INT4 integer schemes requires balancing mathematical precision against target hardware capabilities. Our benchmark breakdown evaluates perplexity impact against raw execution speed on modern compute clusters.
Evaluating Perplexity Degradation Across Formats
FP8 variants preserve dynamic dynamic ranges effective for maintaining reasoning capability in long-form generation tasks. Conversely, INT4 quantization introduces measurable quantization loss in specialized domains such as code generation and mathematical proof parsing. Selecting an optimal format depends directly on whether your application prioritizes generation fidelity or memory efficiency.
Hardware Execution Latency and Kernel Overhead
Theoretical memory bandwidth savings do not instantly translate into wall-clock speedups without optimized CUDA kernels. FP8 tensor core operations match native FP16 execution pipelines on newer GPU architectures with minimal dequantization overhead. INT4 quantization schemes often require custom kernel fusion to prevent memory bus bottlenecks during activation scaling.
Actionable Trade-offs for Deployment Pipelines
Deploy FP8 models when preserving exact downstream evaluation metrics takes priority over raw parameter density. Reserve INT4 for edge deployments or high-concurrency summarization pipelines where latency reductions outweigh micro-perceptions in output quality. Rigorous evaluation metrics on domain-specific test sets must drive format selection before committing to infrastructure changes.