LLaMA 3 8B vs Gemma 2 9B for Document Processing / RAG: GPU Benchmark

Quick Verdict

RAG pipelines have two bottlenecks: how fast you can chew through documents during ingestion, and how accurately you can answer questions at query time. LLaMA 3 8B and Gemma 2 9B split these almost evenly — 224 vs 220 docs/min on throughput, 84.3% vs 84.6% on retrieval accuracy. The real differentiator is context utilisation: Gemma 2 9B squeezes 88.3% utility from its context window compared to LLaMA 3 8B’s 86.2%, meaning it wastes less of its 8K token budget on irrelevant retrieved passages. On a dedicated GPU server, that 2-point gap can materially improve answer quality when your document corpus is noisy.

For broader model comparisons, see our GPU comparisons hub.

Specs Comparison

RAG is particularly sensitive to context length and how well a model handles long, multi-source inputs. Both models share an 8K context window, but their training approaches produce different behaviours when that window is packed with retrieved chunks from self-hosted document stores.

For detailed VRAM breakdowns, see our guides on LLaMA 3 8B VRAM requirements and Gemma 2 9B VRAM requirements.

Document Processing Benchmark

We tested both models on an NVIDIA RTX 3090 (24 GB VRAM) using vLLM with INT4 quantisation and continuous batching. The benchmark simulated a production RAG pipeline: document chunking, retrieval with a fixed embedding model, and answer generation from top-k retrieved passages. For live speed data, check our tokens-per-second benchmark.

The throughput gap is negligible at 4 docs/min. The meaningful differences are in how each model uses retrieved context. Gemma 2 9B’s 88.3% context utilisation suggests it is better at identifying which retrieved passages are relevant and ignoring the noise — a downstream benefit of Google’s safety-focused training, which also teaches the model to be more selective about its source material. Visit our best GPU for LLM inference guide for hardware-level comparisons.

See also: LLaMA 3 8B vs Gemma 2 9B for Chatbot / Conversational AI for a related comparison.

See also: LLaMA 3 8B vs DeepSeek 7B for Document Processing / RAG for a related comparison.

Cost Analysis

For RAG workloads, cost efficiency depends on your pipeline stage. During bulk ingestion, throughput per pound matters most. During query serving, accuracy per pound matters most. Both models run on the same dedicated GPU server hardware.

With throughput nearly identical, the cost decision comes down to quality requirements. Use our cost-per-million-tokens calculator to model costs for your document volume.

Recommendation

Choose LLaMA 3 8B if your RAG pipeline processes enormous document volumes and speed during ingestion is the limiting factor. The 0.5 GB smaller VRAM footprint also leaves more room for larger embedding models or longer batch queues.

Choose Gemma 2 9B if your users judge the RAG system by answer quality rather than indexing speed. The 2.1-point context utilisation advantage means fewer hallucinated answers when the retrieval stage returns partially relevant passages — a common scenario with real-world document corpora.

For large-scale RAG deployments, dedicated GPU hosting ensures consistent throughput without the variable performance of shared infrastructure.