Gemma 4 12B IT Assistant — Hardware Requirements & GPU Compatibility
Chat
Gemma 4 12B IT Assistant is a 12B-parameter open language model from Google in the Gemma 4 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 7.52 GB of VRAM — see which GPUs and Macs can run it below.
29.2K downloads
82 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 12B
- Architecture
- Gemma4UnifiedAssistantForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-05-23
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Gemma 4 12B IT Assistant Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 5.4 GB | 7.5 GB | 5.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 6.2 GB | 8.3 GB | 5.85 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 7.5 GB | 9.7 GB | 7.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 8.9 GB | 11 GB | 8.55 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 10.2 GB | 12.3 GB | 9.90 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 12.3 GB | 14.4 GB | 12.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 24.3 GB | 26.4 GB | 24.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Gemma 4 12B IT Assistant?
Q4_K_M · 7.5 GBGemma 4 12B IT Assistant (Q4_K_M) requires 7.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 262K context window can add up to 2.1 GB, bringing total usage to 9.7 GB. 50 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 4 12B IT Assistant?
Q4_K_M · 7.5 GB55 devices with unified memory can run Gemma 4 12B IT Assistant, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomWhere to Download Gemma 4 12B IT Assistant
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Gemma 4 12B IT Assistant need?
Gemma 4 12B IT Assistant requires 7.5 GB of VRAM at Q4_K_M, or 24.3 GB at BF16. Full 262K context adds up to 2.1 GB (9.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 4.8 bits ÷ 8 = 7.2 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M7.5 GBQ4_K_M + full context9.7 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 12B IT Assistant?
Yes, at Q8_0 (12.3 GB) or lower. Higher quantizations like BF16 (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 4 12B IT Assistant?
For Gemma 4 12B IT Assistant, Q4_K_M (7.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (8.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.4 GB.
VRAM requirement by quantization
Q2_K5.4 GBQ4_K_M ★7.5 GBQ5_K_M8.9 GBQ6_K10.2 GBQ8_012.3 GBBF1624.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 12B IT Assistant on a Mac?
Gemma 4 12B IT Assistant requires at least 5.4 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 4 12B IT Assistant locally?
Yes — Gemma 4 12B IT Assistant can run locally on consumer hardware. At Q4_K_M quantization it needs 7.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 12B IT Assistant?
At Q4_K_M, Gemma 4 12B IT Assistant can reach ~585 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~87 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 7.5 × 0.65 = ~692 tok/s
Estimated speed at Q4_K_M (7.5 GB)
~692 tok/s~87 tok/s~692 tok/s~585 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 4 12B IT Assistant?
At Q4_K_M, the download is about 7.20 GB. The full-precision BF16 version is 24.00 GB. The smallest option (Q2_K) is 5.10 GB.
- Which GPUs can run Gemma 4 12B IT Assistant?
50 consumer GPUs can run Gemma 4 12B IT Assistant at Q4_K_M (7.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 12B IT Assistant?
59 devices with unified memory can run Gemma 4 12B IT Assistant at Q4_K_M (7.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.