Gemma 3 27B IT Qat Q4 0 GGUF — Hardware Requirements & GPU Compatibility
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HuggingFace
How Much VRAM Does Gemma 3 27B IT Qat Q4 0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 14.8 GB | — | 13.50 GB | 4-bit legacy quantization |
Which GPUs Can Run Gemma 3 27B IT Qat Q4 0 GGUF?
Q4_0 · 14.8 GBGemma 3 27B IT Qat Q4 0 GGUF (Q4_0) requires 14.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 3 27B IT Qat Q4 0 GGUF?
Q4_0 · 14.8 GB27 devices with unified memory can run Gemma 3 27B IT Qat Q4 0 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3 27B IT Qat Q4 0 GGUF need?
Gemma 3 27B IT Qat Q4 0 GGUF requires 14.8 GB of VRAM at Q4_0.
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4 bits ÷ 8 = 13.5 GB
KV Cache + Overhead ≈ 1.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_014.8 GB- Can I run Gemma 3 27B IT Qat Q4 0 GGUF on a Mac?
Gemma 3 27B IT Qat Q4 0 GGUF requires at least 14.8 GB at Q4_0, 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 Qat Q4 0 GGUF locally?
Yes — Gemma 3 27B IT Qat Q4 0 GGUF can run locally on consumer hardware. At Q4_0 quantization it needs 14.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 27B IT Qat Q4 0 GGUF?
At Q4_0, Gemma 3 27B IT Qat Q4 0 GGUF can reach ~196 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~44 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 ÷ 14.8 × 0.55 = ~196 tok/s
Estimated speed at Q4_0 (14.8 GB)
AMD Instinct MI300X~196 tok/sNVIDIA GeForce RTX 4090~44 tok/sNVIDIA H100 SXM~147 tok/sAMD Instinct MI250X~121 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 Qat Q4 0 GGUF?
At Q4_0, the download is about 13.50 GB.
- Which GPUs can run Gemma 3 27B IT Qat Q4 0 GGUF?
17 consumer GPUs can run Gemma 3 27B IT Qat Q4 0 GGUF at Q4_0 (14.8 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 3 27B IT Qat Q4 0 GGUF?
27 devices with unified memory can run Gemma 3 27B IT Qat Q4 0 GGUF at Q4_0 (14.8 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.