Gemma 3 1B IT GGUF — Hardware Requirements & GPU Compatibility
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A GGUF-quantized version of Google's Gemma 3 1B Instruct-Tuned, repackaged by Unsloth. At 1 billion parameters, this model sits in the lightweight tier and can run comfortably on virtually any modern hardware, including older GPUs and even CPU-only setups. It offers a meaningful step up from the 270M variant in coherence and instruction following, making it a practical option for simple chat tasks, summarization, and local prototyping where speed and low resource usage matter more than peak quality.
53.3K downloads
80 likesSpecifications
- Publisher
- Unsloth
- Family
- Gemma
- Parameters
- 1B
- Architecture
- Gemma3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2025-05-09
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 3 1B IT GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 0.6 GB | 1.6 GB | 0.28 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 0.7 GB | 1.6 GB | 0.34 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 0.8 GB | 1.7 GB | 0.39 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 0.8 GB | 1.7 GB | 0.42 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.8 GB | 1.7 GB | 0.44 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | 1.8 GB | 0.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.9 GB | 1.8 GB | 0.50 GB | 4-bit legacy quantization |
| IQ4_XS | 4.30 | 0.9 GB | 1.8 GB | 0.54 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 0.9 GB | 1.8 GB | 0.56 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 0.9 GB | 1.8 GB | 0.56 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 0.9 GB | 1.8 GB | 0.56 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 1.0 GB | 1.9 GB | 0.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 1.1 GB | 2.0 GB | 0.69 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.1 GB | 2.0 GB | 0.71 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.2 GB | 2.1 GB | 0.82 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.4 GB | 2.3 GB | 1.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 1B IT GGUF?
Q4_K_M · 1.0 GBGemma 3 1B IT GGUF (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 33K context window can add up to 0.9 GB, bringing total usage to 1.9 GB. 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 GGUF?
Q4_K_M · 1.0 GB33 devices with unified memory can run Gemma 3 1B IT GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3 1B IT GGUF need?
Gemma 3 1B IT GGUF requires 1.0 GB of VRAM at Q4_K_M, or 1.4 GB at Q8_0. Full 33K context adds up to 0.9 GB (1.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M1.0 GBQ4_K_M + full context1.9 GB- What's the best quantization for Gemma 3 1B IT GGUF?
For Gemma 3 1B IT GGUF, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.6 GB.
VRAM requirement by quantization
IQ2_XXS0.6 GB~53%Q3_K_S0.8 GB~77%Q4_10.9 GB~88%Q4_K_M ★1.0 GB~89%Q5_K_S1.1 GB~92%Q8_01.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 1B IT GGUF on a Mac?
Gemma 3 1B IT GGUF requires at least 0.6 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 GGUF locally?
Yes — Gemma 3 1B IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 1B IT GGUF?
At Q4_K_M, Gemma 3 1B IT GGUF can reach ~3037 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~683 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 ÷ 1.0 × 0.55 = ~3037 tok/s
Estimated speed at Q4_K_M (1.0 GB)
AMD Instinct MI300X~3037 tok/sNVIDIA GeForce RTX 4090~683 tok/sNVIDIA H100 SXM~2270 tok/sAMD Instinct MI250X~1877 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 GGUF?
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.