Gemma vs LLaMA 3: Google vs Meta LLM Comparison

Gemma vs LLaMA 3: Google Meets Meta

Google’s Gemma 2 and Meta’s LLaMA 3 represent the best open-weight offerings from two of the world’s largest AI labs. For teams provisioning a dedicated GPU server for LLM inference, the choice between them affects quality, throughput, and long-term ecosystem support. This comparison covers both the 7-9B tier that fits on a single consumer GPU.

Gemma 2 benefits from Google’s distillation techniques and knowledge transfer from larger Gemini models. LLaMA 3 leverages Meta’s massive 15-trillion-token training corpus. For hosting specifics, see our Gemma hosting and LLaMA hosting pages.

Model Specifications

Gemma 2 introduces a novel alternation between local sliding-window attention and full global attention layers, which improves efficiency on longer sequences without increasing VRAM usage linearly. Both models share the same 8K context length.

Benchmark Comparison

Gemma 2 9B leads on general knowledge (MMLU) and reasoning (ARC, Winogrande), likely due to distillation from a larger teacher model. LLaMA 3 8B holds the edge on code generation (HumanEval). The quality gap is meaningful: nearly 5 points on MMLU. For code-focused tasks, you may also want to see our CodeLlama vs DeepSeek Coder comparison.

GPU Inference Performance

Tested on an RTX 3090 using vLLM. See the tokens-per-second benchmark for updated numbers.

LLaMA 3 is faster on inference due to its smaller footprint. Gemma 2’s extra billion parameters and sliding-window attention add overhead but deliver higher quality. On a 24 GB card both run comfortably at FP16. On an RTX 4060 (8 GB), both need 4-bit quantisation.

Self-Hosting Setup

# Gemma 2 9B via Ollama
ollama run gemma2:9b

# LLaMA 3 8B via vLLM
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3-8B-Instruct \
  --dtype float16 --max-model-len 8192

Both are fully supported in vLLM and Ollama. Note that Gemma 2 requires accepting Google’s terms on Hugging Face before downloading. Read our vLLM vs Ollama guide for framework advice and the self-host LLM guide for the full workflow.

Which Model to Deploy

Choose Gemma 2 9B for the best overall quality at the small-model tier, especially on reasoning and general knowledge tasks. Its distillation-based training gives it a quality edge that goes beyond what the parameter count suggests. See our Run Gemma 2 on a Dedicated Server guide for more.

Choose LLaMA 3 8B for faster inference, lower VRAM usage, better code generation, and the largest fine-tuning ecosystem. It is also the safer licensing choice for commercial products.

Compare more models in the GPU comparisons section, or check the best GPU for LLM inference guide.