Gemma 3 12B IT GGUF — Hardware Requirements & GPU Compatibility
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- Publisher
- mradermacher
- Family
- Gemma
- Parameters
- 12B
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 3 12B IT GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 5.6 GB | — | 5.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 5.8 GB | — | 5.25 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 6.4 GB | — | 5.85 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 7.9 GB | — | 7.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 9.4 GB | — | 8.55 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.9 GB | — | 9.90 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 13.2 GB | — | 12.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 12B IT GGUF?
Q4_K_M · 7.9 GBGemma 3 12B IT GGUF (Q4_K_M) requires 7.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 3 12B IT GGUF?
Q4_K_M · 7.9 GB33 devices with unified memory can run Gemma 3 12B IT GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3 12B IT GGUF need?
Gemma 3 12B IT GGUF requires 7.9 GB of VRAM at Q4_K_M, or 13.2 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 4.8 bits ÷ 8 = 7.2 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M7.9 GB- What's the best quantization for Gemma 3 12B IT GGUF?
For Gemma 3 12B IT GGUF, Q4_K_M (7.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (9.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.6 GB.
VRAM requirement by quantization
Q2_K5.6 GB~75%Q3_K_L6.8 GB~86%Q4_K_S7.4 GB~88%Q4_K_M ★7.9 GB~89%Q5_K_M9.4 GB~92%Q8_013.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 12B IT GGUF on a Mac?
Gemma 3 12B IT GGUF requires at least 5.6 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 12B IT GGUF locally?
Yes — Gemma 3 12B IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 7.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 12B IT GGUF?
At Q4_K_M, Gemma 3 12B IT GGUF can reach ~368 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~83 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 ÷ 7.9 × 0.55 = ~368 tok/s
Estimated speed at Q4_K_M (7.9 GB)
AMD Instinct MI300X~368 tok/sNVIDIA GeForce RTX 4090~83 tok/sNVIDIA H100 SXM~275 tok/sAMD Instinct MI250X~228 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 12B IT GGUF?
At Q4_K_M, the download is about 7.20 GB. The full-precision Q8_0 version is 12.00 GB. The smallest option (Q2_K) is 5.10 GB.
- Which GPUs can run Gemma 3 12B IT GGUF?
35 consumer GPUs can run Gemma 3 12B IT GGUF at Q4_K_M (7.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 3 12B IT GGUF?
33 devices with unified memory can run Gemma 3 12B IT GGUF at Q4_K_M (7.9 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.