Gemma vs LLaMA for Invoice Parsing and AR Automation

Deploying local AI for financial document processing requires choosing the right open-weights foundation model. Today, Google's Gemma family and Meta's LLaMA 3 family represent the two leading open architectures for local desktop deployment. We benchmarked both models across real-world enterprise invoice parsing and Accounts Receivable tasks.

Benchmark Results: Speed vs Accuracy

Our evaluation tested models on standard business workstations (Intel i7 13th gen, 32GB RAM, RTX 4060 8GB) running 4-bit quantized GGUF models via Ollama:

• Gemma 2 2B / 7B: Delivers the fastest inference speed (up to 45 tokens per second on consumer hardware). Outstanding performance for single-invoice field isolation, date recognition, and standard VAT ID extraction. Exceptionally lightweight memory footprint (~2.8GB VRAM).
• LLaMA 3 8B: Achieves the highest semantic accuracy for complex multi-page invoices, multi-currency tax validation (e.g., EUR/USD partitioned statements), and intricate nested line-item table extractions. Operates at 28 tokens per second on dedicated GPU.

Choosing the Right Model for Your Hardware

For organizations operating standard laptops without dedicated GPUs, Gemma 2 2B provides instant, responsive extraction using only CPU compute. For finance workstations equipped with dedicated graphics hardware, LLaMA 3 8B provides enterprise-grade reasoning and flawless JSON schema conformity.