gemma-3-1b-it
Q4 GGUF download: ggml-org/gemma-3-1b-it-GGUF - 0.81 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
4.8
AA Index
Intelligence Index - gemma-3-1b-it vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-19.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.00 |
| Output (per 1M tokens) | $0.00 |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
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
vllm serve google/gemma-3-1b-itNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
gemma-3-1b-it
gemma-3-1b-it is a 1B-parameter gemma model from google. At Q4_K_M it needs roughly 1 GB of VRAM, placing it in the cpu hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 1B |
| License | gemma |
| Modalities | text |
| Released | 2025-03-10 |
| Weights | google/gemma-3-1b-it |
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~1 GB | RTX 4060, RTX 3060 8GB |
| Q5_K_M | ~1 GB | RTX 4060, RTX 3060 8GB |
| Q8_0 | ~1 GB | RTX 4060, RTX 3060 8GB |
| FP16 | ~2 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-3-1b-itPopularity
gemma-3-1b-it has 1,692,600 downloads in the last month on HuggingFace and 983 likes.
Frequently asked
Quick answers to common questions
How much VRAM does gemma-3-1b-it need?
gemma-3-1b-it with 1B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is gemma-3-1b-it better than other google models?
gemma-3-1b-it has 1B parameters with 8,192 context - a strong choice for general use.
What license is gemma-3-1b-it under?
gemma-3-1b-it is released under the gemma license, making it suitable for most commercial and personal projects.
What hardware runs gemma-3-1b-it well?
With 1B parameters, gemma-3-1b-it 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-3-1b-it?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with gemma-3-1b-it?
gemma-3-1b-it 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