gemma-3-270m
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
7.7
AA Index
Coding
0.0
AA Index
Agentic
3.0
AA Index
Math
2.3
AA Index
Intelligence Index - gemma-3-270m vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Math Index comparison
Benchmark data from Artificial Analysis · updated 2026-06-07.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLUPRO | 5.5 |
| GPQA | 22.4 |
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
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Deep dive
Notes, sources, and the full write-up
gemma-3-270m
gemma-3-270m is a 0.3B-parameter gemma model from google. It scores 7.7 on the Artificial Analysis Intelligence Index (coding 0). At Q4_K_M it needs roughly 0 GB of VRAM, placing it in the cpu hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 0.3B |
| License | gemma |
| Modalities | text |
| Released | 2025-08-05 |
| Weights | google/gemma-3-270m |
Benchmarks
Artificial Analysis Intelligence Index - gemma-3-270m vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| gemma-3-270m | 7.7 | 0 | 22.4 |
| GPT-5.5 (xhigh) | 60.2 | 59.1 | 93.5 |
| Claude Opus 4.8 (max) | 61.4 | 56.7 | 92 |
| Gemini 3.1 Pro Preview | 57.2 | 55.5 | 94.1 |
| Grok 4.3 (high) | 53.2 | 41 | 90.1 |
Source: Artificial Analysis (2026-06-04).
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~0 GB | RTX 4060, RTX 3060 8GB |
| Q5_K_M | ~0 GB | RTX 4060, RTX 3060 8GB |
| Q8_0 | ~0 GB | RTX 4060, RTX 3060 8GB |
| FP16 | ~1 GB | RTX 4060, RTX 3060 8GB |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve google/gemma-3-270mPopularity
gemma-3-270m has 7,073,287 downloads in the last month on HuggingFace and 1,033 likes.
Frequently asked
Quick answers to common questions
How much VRAM does gemma-3-270m need?
gemma-3-270m with 0.3B parameters needs approximately 0 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is gemma-3-270m better than other google models?
gemma-3-270m has 0.3B parameters with 8,192 context - a strong choice for general use.
What license is gemma-3-270m under?
gemma-3-270m is released under the gemma license, making it suitable for most commercial and personal projects.
What hardware runs gemma-3-270m well?
With 0.3B parameters, gemma-3-270m requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for gemma-3-270m?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~0 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with gemma-3-270m?
gemma-3-270m competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec
Similar by size
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