Gemma 3 1B: VRAM requirements and GPUs that run it

Memory needed by quantization

Totals include about 10% runtime overhead and the KV cache. Its conversation memory (KV cache) is unusually small, so going from 8K to 32K context adds only about 0.7 GB. Most people should download Q4_K_M; Q5 or Q8 only make sense if you have spare memory. Read which quantization to pick.

Which GPUs can run Gemma 3 1B?

40 of the 40 cards we track run it fully in VRAM at 8K context. The cheapest entry point is a 4 GB card such as the GTX 1650, and the fastest is the RTX 5090 at about 1100 tokens per second.

Running it on a Mac

Apple Silicon Macs can give a model about 72% of their unified memory. Gemma 3 1B fits from 8 GB (Apple M1, about 42 tokens per second). With a faster chip like the Apple M3 Ultra (96 GB) expect around 500 tokens per second. Compare the options in our Mac vs NVIDIA guide.

Similar size models: Qwen3 0.6B, Llama 3.2 1B.

Compare Gemma 3 1B

Questions

Estimates based on model size, architecture and memory bandwidth. Real numbers vary with software, drivers and context length.