Local LLM Performance Affected by Hardware and Software Variations

Aug 22, 2026

AI Summary

The performance of local large language models (LLMs) can vary significantly based on the specific hardware and software configurations used. This variability impacts the models' ability to generate accurate outputs, as demonstrated through various benchmarking experiments that highlight the influence of different CUDA kernels and quantization methods on token generation.

Local LLMs exhibit performance differences due to variations in hardware and software setups, including GPU generations and instruction sets.
Standard benchmarks should be used to evaluate LLM performance, focusing on representative workloads rather than simplistic tests.
The concept of KL Divergence (KLD) is introduced as a measure of how output probabilities differ from a baseline, emphasizing the importance of methodology in interpreting KLD results.
Experiments conducted on the Qwen3.6-27B model reveal that different attention backends can lead to token generation discrepancies, particularly during tool calls.
The choice of quantization methods for weights and activations significantly affects performance, with some configurations leading to higher rates of token flips and errors in execution.
The findings underscore the necessity for precise measurement and testing in LLM implementations to avoid critical errors in production environments.

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Local LLM Performance Affected by Hardware and Software Variations

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