Gemma 3 27B IT — Hardware Requirements & GPU Compatibility
Vision
Google Gemma 3 27B IT is a 27.4-billion parameter multimodal instruction-tuned model from Google's Gemma 3 family. It supports both text and image inputs, making it one of the most capable openly available vision-language models for local inference. The model handles conversational AI, visual question answering, image description, and complex reasoning tasks across modalities. Gemma 3 27B IT requires a GPU with at least 24GB of VRAM for quantized inference, placing it within reach of high-end consumer cards like the RTX 4090. It uses a dense Transformer architecture with a large context window and benefits from Google's extensive pretraining pipeline. Released under the Gemma license.
1.2M downloads
1.9K likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 27.4B
- Context Length
- 131,072 tokens
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 3 27B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 8.3 GB | — | 7.54 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 10.2 GB | — | 9.26 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 11.7 GB | — | 10.63 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 12.8 GB | — | 11.66 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.2 GB | — | 12.00 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 14.7 GB | — | 13.37 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 15.1 GB | — | 13.72 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 15.5 GB | — | 14.06 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 16.2 GB | — | 14.74 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 17.0 GB | — | 15.43 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 17.0 GB | — | 15.43 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 17.0 GB | — | 15.43 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 18.1 GB | — | 16.46 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 20.8 GB | — | 18.86 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 21.5 GB | — | 19.55 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 24.9 GB | — | 22.63 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 30.2 GB | — | 27.43 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 27B IT?
Q4_K_M · 18.1 GBGemma 3 27B IT (Q4_K_M) requires 18.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 3 27B IT?
Q4_K_M · 18.1 GB21 devices with unified memory can run Gemma 3 27B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (6)
Frequently Asked Questions
- How much VRAM does Gemma 3 27B IT need?
Gemma 3 27B IT requires 18.1 GB of VRAM at Q4_K_M, or 30.2 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 27.4B × 4.8 bits ÷ 8 = 16.5 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M18.1 GB- Can NVIDIA GeForce RTX 4090 run Gemma 3 27B IT?
Yes, at Q5_K_M (21.5 GB) or lower. Higher quantizations like Q6_K (24.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 3 27B IT?
For Gemma 3 27B IT, Q4_K_M (18.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (20.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.3 GB.
VRAM requirement by quantization
IQ2_XXS8.3 GB~53%Q3_K_S13.2 GB~77%IQ4_XS16.2 GB~87%Q4_K_M ★18.1 GB~89%Q5_K_S20.8 GB~92%Q8_030.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 27B IT on a Mac?
Gemma 3 27B IT requires at least 8.3 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 27B IT locally?
Yes — Gemma 3 27B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 18.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 27B IT?
At Q4_K_M, Gemma 3 27B IT can reach ~161 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~36 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 ÷ 18.1 × 0.55 = ~161 tok/s
Estimated speed at Q4_K_M (18.1 GB)
AMD Instinct MI300X~161 tok/sNVIDIA GeForce RTX 4090~36 tok/sNVIDIA H100 SXM~120 tok/sAMD Instinct MI250X~100 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 27B IT?
At Q4_K_M, the download is about 16.46 GB. The full-precision Q8_0 version is 27.43 GB. The smallest option (IQ2_XXS) is 7.54 GB.