PaliGemma 2 3B

Positioning

PaliGemma 2 3B is a dense vision-language model from Google, built on the Gemma 2 language backbone paired with a SigLIP vision encoder. Released under the Gemma License, it is designed specifically as a base for fine-tuning on task-specific vision tasks. With 3 billion parameters and an 8,192-token context window, it occupies a compact footprint in the VLM space, making it accessible for experimentation and specialized deployment.

Strengths

• Compact and efficient architecture: As a dense 3B-parameter model, PaliGemma 2 3B requires modest compute and memory, enabling fine-tuning and inference on consumer-grade hardware.
• Designed for fine-tuning: The combination of Gemma 2 and SigLIP is purpose-built for transfer learning on specific vision tasks, offering a strong starting point for custom applications.
• Permissive licensing for many use cases: The Gemma License allows for broad commercial and research use, though users should verify terms for their specific deployment.
• Small quantized sizes enable local deployment: At Q4_K_M, the model is only ~1.7 GB on disk, and even the full FP16 version is ~6 GB, making it feasible to run on a single consumer GPU with adequate VRAM.

Limitations

• Limited context window: With only 8,192 tokens, the model is not suited for tasks requiring long-form reasoning or processing large documents or images.
• Small parameter count: At 3B parameters, the model may lack the capacity for complex multi-step reasoning or high-accuracy performance on challenging benchmarks compared to larger VLMs.
• Task-specific design: PaliGemma 2 is intended as a fine-tuning base, not a general-purpose out-of-the-box VLM. Operators expecting strong zero-shot performance may be disappointed.
• No community benchmarks available: We do not have verified independent benchmark results for this model. Published vendor metrics should be treated as best-case, and operators should evaluate on their own data.

What it takes to run this locally

PaliGemma 2 3B fits comfortably in the consumer deployment class. Quantized sizes range from ~6 GB (FP16) down to ~1.0 GB (Q2_K). For typical use with a moderate context, add ~30-50% for KV cache and framework overhead. A single GPU with 8-12 GB VRAM (e.g., RTX 3060 or higher) can run the model at Q4_K_M or Q5_K_M quantizations. For FP16, a 12 GB GPU is recommended. No specific tokens-per-second measurements are available.

Should you run this locally?

Yes if you need a compact, fine-tunable VLM for a specific vision task and have access to a consumer GPU with at least 8 GB VRAM. The small size and permissive license make it ideal for prototyping and custom deployment.

No if you require a large context window, out-of-the-box general vision-language capabilities, or state-of-the-art performance without fine-tuning. Consider larger models or those with broader zero-shot abilities.

Catalog cross-links

• Gemma 2 9B
• SigLIP
• Consumer GPU Guide