Gemma 2 for Document Summarisation: GPU Requirements & Setup

The Hallucination Problem in Document Summarisation

A law firm summarises 300 contracts a week. A single hallucinated clause — a fabricated indemnity limit, an invented termination date — can cascade into a flawed legal opinion that exposes the firm to malpractice liability. That is why raw summarisation speed is the wrong metric for regulated industries. Factual fidelity is what matters, and Gemma 2 was designed around it.

Google’s grounding capability ensures that Gemma 2 summaries reflect what the source document actually says, without embellishment or fabrication. For legal review, medical literature synthesis and financial reporting, that reliability translates directly into fewer corrections and lower risk.

Self-hosting on dedicated GPU servers keeps privileged documents off third-party infrastructure. A Gemma 2 hosting deployment gives you deterministic throughput and flat-rate billing with no per-token surprises.

Hardware Recommendations

Summarisation workloads consume long input contexts and generate shorter outputs. VRAM needs scale with document length. The configurations below are validated for production use. See the best GPU for inference guide for broader context.

Check availability on the document AI hosting page or in the full dedicated GPU hosting catalogue.

Getting Gemma 2 Running

Provision a GigaGPU server, connect over SSH, and launch the model with vLLM. The 8192-token context window accommodates most single-document summarisation tasks:

# Launch Gemma 2 for document summarisation
pip install vllm
python -m vllm.entrypoints.openai.api_server \
  --model google/gemma-2-9b-it \
  --max-model-len 8192 \
  --port 8000

Feed documents through the OpenAI-compatible endpoint from any language or framework. For an alternative summarisation approach, see Qwen 2.5 for Document Summarisation.

Throughput & Accuracy Metrics

On an RTX 5090, Gemma 2 processes roughly 520 single-page documents per hour. The headline number is modest compared to lighter models, but the effective throughput is higher once you factor in the reduced correction burden. Fewer hallucinated summaries means fewer hours of professional staff time spent re-reading source material.

Results vary with quantisation and document complexity. Detailed GPU-tier comparisons are in the Gemma benchmarks. Also see Phi-3 for Document Summarisation for a speed-optimised alternative.

Return on Investment

Consider the cost of a single factual error in a legal summary or a financial brief: professional review hours, potential client exposure, regulatory scrutiny. Gemma 2 cannot eliminate errors entirely, but its grounding dramatically reduces the rate at which they occur. That avoided rework often dwarfs the server cost.

An RTX 5090 server runs between GBP 1.50 and 4.00 per hour with no per-token fees. Teams handling higher volumes will find the RTX 6000 Pro tier delivers stronger per-document economics and absorbs traffic spikes. Browse current rates on the GPU server pricing page.