LLaMA 3 8B vs Gemma 2 9B for Cost-Optimised Batch Processing: GPU Benchmark

Quick Verdict

Batch processing flips the usual LLM evaluation criteria upside down. Nobody is waiting for a response in real time, so latency is irrelevant. What matters is how many tokens you can push through the GPU per pound — and here, the numbers are closer than you might expect. LLaMA 3 8B moves 368 batch tok/s at $0.07 per million tokens. Gemma 2 9B moves 328 batch tok/s at $0.15 per million. LLaMA 3 8B wins on both speed and cost, but Gemma 2 9B achieves 94% GPU utilisation versus 89%, suggesting it extracts more from the hardware even if the total throughput is lower. On a dedicated GPU server, the choice depends on whether you optimise for wall-clock time or GPU efficiency.

For broader model comparisons, see our GPU comparisons hub.

Specs Comparison

Batch workloads are less sensitive to architectural differences than real-time serving, but VRAM footprint still matters: a smaller model leaves more room for larger batch sizes, which directly improves throughput. Here is how these models compare for self-hosted deployment.

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

Batch Processing Benchmark

We tested both models on an NVIDIA RTX 3090 (24 GB VRAM) using vLLM with INT4 quantisation, maximum batch sizes, and continuous batching. The workload simulated overnight processing of classification, summarisation, and extraction tasks. For live speed data, check our tokens-per-second benchmark.

Gemma 2 9B’s 94% GPU utilisation is notably higher, meaning it leaves less compute on the table even though its absolute throughput is lower. This is a consequence of the larger model saturating the GPU’s compute units more completely. For batch workloads where you are paying for the server regardless of utilisation, the absolute throughput matters more than efficiency percentages. 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 Cost-Optimised Batch Processing for a related comparison.

Cost Analysis

Batch processing is where cost differences compound fastest. A 12% throughput gap applied to millions of tokens processed overnight turns into significant monthly savings on the same dedicated GPU server.

The $0.07 vs $0.15 per million tokens gap is the real headline — LLaMA 3 8B processes tokens at less than half the cost. Use our cost-per-million-tokens calculator to project monthly costs at your expected batch volume.

Recommendation

Choose LLaMA 3 8B for large-scale overnight batch jobs — content classification pipelines, bulk summarisation, data extraction from document archives. The 12% throughput advantage and 2x cost efficiency add up quickly when you are processing millions of tokens per run.

Choose Gemma 2 9B if your batch output will be customer-facing and quality justifies the cost premium. Google’s safety-aligned tuning produces more carefully worded output that may require less post-processing for tasks like bulk content generation or automated report writing.

Run batch workloads overnight on dedicated GPU servers to maximise utilisation and minimise cost per processed unit.