Gemma 3 27B IT GGUF — Hardware Requirements & GPU Compatibility
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114.4K downloads
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- Publisher
- MaziyarPanahi
- Family
- Gemma
- Parameters
- 27B
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HuggingFace
How Much VRAM Does Gemma 3 27B IT GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 12.6 GB | — | 11.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.0 GB | — | 11.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 14.5 GB | — | 13.16 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 15.2 GB | — | 13.84 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 16.7 GB | — | 15.19 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 17.8 GB | — | 16.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 20.4 GB | — | 18.56 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 21.2 GB | — | 19.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 24.5 GB | — | 22.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 29.7 GB | — | 27.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 27B IT GGUF?
Q4_K_M · 17.8 GBGemma 3 27B IT GGUF (Q4_K_M) requires 17.8 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 GGUF?
Q4_K_M · 17.8 GB21 devices with unified memory can run Gemma 3 27B IT GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3 27B IT GGUF need?
Gemma 3 27B IT GGUF requires 17.8 GB of VRAM at Q4_K_M, or 29.7 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M17.8 GB- Can NVIDIA GeForce RTX 4090 run Gemma 3 27B IT GGUF?
Yes, at Q5_K_M (21.2 GB) or lower. Higher quantizations like Q6_K (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 3 27B IT GGUF?
For Gemma 3 27B IT GGUF, Q4_K_M (17.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (20.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.6 GB.
VRAM requirement by quantization
Q2_K12.6 GB~75%Q3_K_M14.5 GB~83%Q4_K_M ★17.8 GB~89%Q5_K_S20.4 GB~92%Q5_K_M21.2 GB~92%Q8_029.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 27B IT GGUF on a Mac?
Gemma 3 27B IT GGUF requires at least 12.6 GB at Q2_K, 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 GGUF locally?
Yes — Gemma 3 27B IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 17.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 27B IT GGUF?
At Q4_K_M, Gemma 3 27B IT GGUF can reach ~164 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.8 × 0.55 = ~164 tok/s
Estimated speed at Q4_K_M (17.8 GB)
AMD Instinct MI300X~164 tok/sNVIDIA GeForce RTX 4090~37 tok/sNVIDIA H100 SXM~122 tok/sAMD Instinct MI250X~101 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 GGUF?
At Q4_K_M, the download is about 16.20 GB. The full-precision Q8_0 version is 27.00 GB. The smallest option (Q2_K) is 11.47 GB.