Testing & Performance Tuning

[thejhh]

Thoroughly test the end-to-end model. Start with functional tests: input a known prompt and verify the output is coherent (and ideally compare a few outputs to an official implementation or provided examples). Debug any discrepancies in tokenization or decoding. Next, evaluate performance: measure inference latency for a single-thread vs multi-thread execution to ensure that goroutines are providing speedup. If not scaling well, adjust the workload partitioning granularity or reduce synchronization overhead. Tune the number of goroutines (e.g., match the number of CPU cores) for optimal throughput. Monitor memory usage to confirm it remains around the expected ~0.4 GB for the 2B model (the quantized model is very memory-efficient
medium.com
).

Finally, note that the official C++ implementation achieved up to ~6× speedups on x86 CPUs with multi-threading
github.com
– while Go’s performance may differ, strive to approach efficient parallel utilization of the CPU. With all tests passing and performance optimized, the pure Go BitNet inference engine is complete and ready for use.