Gemma 3 12B IT — Hardware Requirements & GPU Compatibility
Vision
Google Gemma 3 12B IT is a 12-billion parameter multimodal instruction-tuned model from Google's Gemma 3 series. It supports both text and image inputs, offering vision-language capabilities at a more accessible size point than the 27B variant. Gemma 3 12B IT runs on consumer GPUs with 12-16GB of VRAM in quantized formats, making it a practical choice for local multimodal inference without requiring top-tier hardware. Released under the Gemma license.
1.9M downloads
678 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 12B
- Context Length
- 32,768 tokens
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 3 12B IT 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_0 | 4.00 | 6.6 GB | — | 6.00 GB | 4-bit legacy 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?
Q4_K_M · 7.9 GBGemma 3 12B IT (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?
Q4_K_M · 7.9 GB33 devices with unified memory can run Gemma 3 12B IT, 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
Derivatives (8)
Frequently Asked Questions
- How much VRAM does Gemma 3 12B IT need?
Gemma 3 12B IT 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?
For Gemma 3 12B IT, 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 IQ2_XXS at 3.6 GB.
VRAM requirement by quantization
IQ2_XXS3.6 GBQ2_K5.6 GBQ3_K_L6.8 GBIQ4_NL7.4 GBQ4_K_M ★7.9 GBQ8_013.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 12B IT on a Mac?
Gemma 3 12B IT requires at least 3.6 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 12B IT locally?
Yes — Gemma 3 12B IT 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?
At Q4_K_M, Gemma 3 12B IT 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)
~368 tok/s~83 tok/s~275 tok/s~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?
At Q4_K_M, the download is about 7.20 GB. The full-precision Q8_0 version is 12.00 GB. The smallest option (IQ2_XXS) is 3.30 GB.
- Which GPUs can run Gemma 3 12B IT?
35 consumer GPUs can run Gemma 3 12B IT 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?
33 devices with unified memory can run Gemma 3 12B IT 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.