Efficient code generation requires a balance between model quality and computational cost, particularly when experiments are performed on resource-constrained hardware. This work investigates the effect of domain-specific Byte Pair Encoding (BPE) on a compact decoder-only Transformer for Python code generation. An 8,000-token Python-specific BPE tokenizer is compared with the 50,258-token GPT-2 tokenizer using the same compact architecture, consisting of two Transformer layers, four attention heads, a model dimension of 128, a feed-forward dimension of 512, and a maximum sequence length of 256 tokens. Experiments use 15,000 training functions, 2,000 validation functions, and 3,000 independent test functions selected with repository-level separation and cross-split duplicate-code verification. On the independent test set, the Python-specific tokenizer reduces average tokenization length by 41.94% and training blocks by 33.89%. The resulting model contains 81.37% fewer parameters, requires 93.79% less training time, and uses 72.40% less peak GPU memory during test evaluation than the GPT-2-tokenizer configuration. However, these efficiency gains are accompanied by lower language-model and code-generation performance. The Custom-BPE model obtains a test perplexity of 125.14, compared with 15.68 for the GPT-2 configuration. These perplexity values are measured over their respective token distributions and are therefore not directly comparable across vocabularies. Under controlled deterministic generation, the Custom-BPE and GPT-2 models produce syntactically valid and compilable outputs in 0% and 26% of cases, respectively. The results therefore characterize an explicit efficiency-versus-generation-quality trade-off rather than a universal quality advantage for domain-specific tokenization.