Gemma 3 1B IT — Hardware Requirements & GPU Compatibility
Chat
Google Gemma 3 1B IT is a 1-billion parameter instruction-tuned model from Google's Gemma 3 family. It is an ultra-compact text-only chat model designed for deployment on minimal hardware, including low-VRAM GPUs and edge devices. The model handles basic conversational tasks, simple instruction following, and lightweight text generation. It can run on virtually any modern GPU and even on CPU-only setups with acceptable latency. Released under the Gemma license.
3.3M downloads
877 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 1B
- Context Length
- 32,768 tokens
- Release Date
- 2025-04-04
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 3 1B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 0.3 GB | — | 0.28 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 0.4 GB | — | 0.34 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 0.4 GB | — | 0.39 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 0.5 GB | — | 0.41 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 0.5 GB | — | 0.42 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.5 GB | — | 0.44 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 0.5 GB | — | 0.45 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 0.5 GB | — | 0.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.6 GB | — | 0.50 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 0.6 GB | — | 0.51 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.6 GB | — | 0.54 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 0.6 GB | — | 0.56 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 0.6 GB | — | 0.56 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 0.6 GB | — | 0.56 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 0.7 GB | — | 0.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 0.7 GB | — | 0.61 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 0.8 GB | — | 0.69 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 0.8 GB | — | 0.71 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 0.8 GB | — | 0.72 GB | 5-bit large quantization |
| Q6_K | 6.60 | 0.9 GB | — | 0.82 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.1 GB | — | 1.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 1B IT?
Q4_K_M · 0.7 GBGemma 3 1B IT (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 3 1B IT?
Q4_K_M · 0.7 GB33 devices with unified memory can run Gemma 3 1B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (8)
Frequently Asked Questions
- How much VRAM does Gemma 3 1B IT need?
Gemma 3 1B IT requires 0.7 GB of VRAM at Q4_K_M, or 1.1 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M0.7 GB- What's the best quantization for Gemma 3 1B IT?
For Gemma 3 1B IT, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.3 GB.
VRAM requirement by quantization
IQ2_XXS0.3 GB~53%Q3_K_S0.5 GB~77%IQ4_XS0.6 GB~87%Q4_K_M ★0.7 GB~89%Q4_K_L0.7 GB~90%Q8_01.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 1B IT on a Mac?
Gemma 3 1B IT requires at least 0.3 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Gemma 3 1B IT locally?
Yes — Gemma 3 1B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 1B IT?
At Q4_K_M, Gemma 3 1B IT can reach ~4417 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~993 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 0.7 × 0.55 = ~4417 tok/s
Estimated speed at Q4_K_M (0.7 GB)
AMD Instinct MI300X~4417 tok/sNVIDIA GeForce RTX 4090~993 tok/sNVIDIA H100 SXM~3301 tok/sAMD Instinct MI250X~2731 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 3 1B IT?
At Q4_K_M, the download is about 0.60 GB. The full-precision Q8_0 version is 1.00 GB. The smallest option (IQ2_XXS) is 0.28 GB.