Gemma 2 vs Gemma 1: Google’s Model Evolution

Google shipped Gemma 1 as a capable but limited family — two sizes, decent benchmarks, restrictive context. Gemma 2 rewrites the lineup with three sizes, a new knowledge distillation approach, and sliding window attention that changes how the model handles long sequences. For self-hosted deployments on dedicated GPU servers, the evolution is substantial enough to warrant a fresh evaluation.

Architecture Differences

The shift from Multi-Query to Grouped-Query Attention reduces KV-cache memory without sacrificing quality. Gemma 2 alternates between sliding window attention (4K) and full attention across layers, giving it local and global context awareness. The logit soft-capping mechanism prevents extreme probability spikes that can cause repetitive or degenerate outputs.

Knowledge distillation is the quiet revolution here. The 9B and 27B models learned from a larger teacher model, which means they punch above their parameter count on quality benchmarks — a pattern that changes the size-versus-quality trade-off. See our Gemma 2 size selection guide for detailed comparisons.

Benchmark Comparison

Gemma 2 9B versus Gemma 1 7B is the most telling comparison. Despite only a 29% parameter increase, MMLU jumps 7 points, HumanEval nearly doubles, and GSM8K climbs 22 points. The distillation approach delivers disproportionate quality gains. Validate these numbers against your workload using the tokens-per-second benchmark.

VRAM and Hosting Requirements

Gemma 2 27B at INT4 fits on a single RTX 5090 with headroom for a reasonable KV-cache. That puts 27B-class quality within reach of a single-GPU deployment — a tier that previously required multi-card setups or RTX 6000 Pro hardware.

Migration Considerations

Gemma 2 is not a drop-in replacement for Gemma 1. The attention mechanism changes mean different KV-cache behaviour, and the logit soft-capping requires serving framework support.

• vLLM 0.5.0+ and TGI 2.0+ both support Gemma 2 natively including soft-capping.
• The chat template changed — update prompt formatting if you use custom templates rather than the tokeniser’s built-in template.
• GQA reduces KV-cache size by roughly 4x compared to standard MHA, which means higher concurrent batch sizes on the same hardware.
• Test quality carefully if migrating from 7B to 9B — the distilled 9B behaves differently on edge cases despite higher aggregate scores.

Which to Deploy

Gemma 2 is the clear choice for new deployments. The 9B model is the sweet spot: it runs on a single RTX 3090, benchmarks competitively with models twice its size, and benefits from distillation-driven quality gains. Gemma 1 should only persist in production where revalidation costs are prohibitive.

Compare against other model families: Phi-3.5 for the smallest deployments, Qwen 2.5 for broader language coverage, and LLaMA 3.1 for maximum ecosystem support. Our best GPU for inference guide covers hardware pairing for each.