Gemma 3 270M IT GGUF — Hardware Requirements & GPU Compatibility
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A GGUF-quantized version of Google's Gemma 3 270M Instruct-Tuned, repackaged by Unsloth. With just 270 million parameters, this is one of the smallest instruction-tuned models available, making it an excellent choice for experimentation, testing inference pipelines, or running on extremely resource-constrained hardware. Don't expect strong reasoning or complex generation from a model this size, but it can handle simple completions and basic instruction following with remarkably low memory requirements.
71.7K downloads
154 likesSpecifications
- Publisher
- Unsloth
- Family
- Gemma
- Parameters
- 270M
- Release Date
- 2025-08-15
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 3 270M IT GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 0.1 GB | — | 0.07 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 0.1 GB | — | 0.09 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 0.1 GB | — | 0.10 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 0.1 GB | — | 0.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.1 GB | — | 0.12 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.1 GB | — | 0.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.1 GB | — | 0.14 GB | 4-bit legacy quantization |
| IQ4_XS | 4.30 | 0.2 GB | — | 0.15 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 0.2 GB | — | 0.15 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 0.2 GB | — | 0.15 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 0.2 GB | — | 0.15 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 0.2 GB | — | 0.16 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 0.2 GB | — | 0.19 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 0.2 GB | — | 0.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.3 GB | — | 0.22 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.3 GB | — | 0.27 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 270M IT GGUF?
Q4_K_M · 0.2 GBGemma 3 270M IT GGUF (Q4_K_M) requires 0.2 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 270M IT GGUF?
Q4_K_M · 0.2 GB33 devices with unified memory can run Gemma 3 270M 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 270M IT GGUF need?
Gemma 3 270M IT GGUF requires 0.2 GB of VRAM at Q4_K_M, or 0.3 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 270M × 4.8 bits ÷ 8 = 0.2 GB
VRAM usage by quantization
Q4_K_M0.2 GB- What's the best quantization for Gemma 3 270M IT GGUF?
For Gemma 3 270M IT GGUF, Q4_K_M (0.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.1 GB.
VRAM requirement by quantization
IQ2_XXS0.1 GB~53%Q3_K_S0.1 GB~77%Q4_10.2 GB~88%Q4_K_M ★0.2 GB~89%Q5_K_S0.2 GB~92%Q8_00.3 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 270M IT GGUF on a Mac?
Gemma 3 270M IT GGUF requires at least 0.1 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 270M IT GGUF locally?
Yes — Gemma 3 270M IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 0.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 270M IT GGUF?
At Q4_K_M, Gemma 3 270M IT GGUF can reach ~16194 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~3640 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 ÷ 0.2 × 0.55 = ~16194 tok/s
Estimated speed at Q4_K_M (0.2 GB)
AMD Instinct MI300X~16194 tok/sNVIDIA GeForce RTX 4090~3640 tok/sNVIDIA H100 SXM~12104 tok/sAMD Instinct MI250X~10012 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 270M IT GGUF?
At Q4_K_M, the download is about 0.16 GB. The full-precision Q8_0 version is 0.27 GB. The smallest option (IQ2_XXS) is 0.07 GB.