Gemma 4 12B IT NVFP4 — Hardware Requirements & GPU Compatibility
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Gemma 4 12B IT NVFP4 is a 7.7B-parameter open language model from vrfai in the Gemma family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.68 GB of VRAM — see which GPUs and Macs can run it below.
787 downloads
3 likesSpecifications
- Publisher
- vrfai
- Family
- Gemma
- Parameters
- 7.7B
- Architecture
- Gemma4UnifiedForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-06
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HuggingFace
How Much VRAM Does Gemma 4 12B IT NVFP4 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.3 GB | 51.9 GB | 3.28 GB | 2-bit quantization with K-quant improvements |
| Q4_K_M | 4.80 | 5.7 GB | 53.3 GB | 4.63 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 7.4 GB | 55.0 GB | 6.36 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | 56.3 GB | 7.71 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 12B IT NVFP4?
Q4_K_M · 5.7 GBGemma 4 12B IT NVFP4 (Q4_K_M) requires 5.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 47.6 GB, bringing total usage to 53.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 4 12B IT NVFP4?
Q4_K_M · 5.7 GB33 devices with unified memory can run Gemma 4 12B IT NVFP4, 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 4 12B IT NVFP4 need?
Gemma 4 12B IT NVFP4 requires 5.7 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0. Full 131K context adds up to 47.6 GB (53.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.7B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 48.7 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.7 GBQ4_K_M + full context53.3 GB- What's the best quantization for Gemma 4 12B IT NVFP4?
For Gemma 4 12B IT NVFP4, Q4_K_M (5.7 GB) offers the best balance of quality and VRAM usage. Q6_K (7.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.3 GB.
VRAM requirement by quantization
Q2_K4.3 GBQ4_K_M ★5.7 GBQ6_K7.4 GBQ8_08.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 12B IT NVFP4 on a Mac?
Gemma 4 12B IT NVFP4 requires at least 4.3 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 NVFP4 locally?
Yes — Gemma 4 12B IT NVFP4 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 12B IT NVFP4?
At Q4_K_M, Gemma 4 12B IT NVFP4 can reach ~513 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~115 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 ÷ 5.7 × 0.55 = ~513 tok/s
Estimated speed at Q4_K_M (5.7 GB)
~513 tok/s~115 tok/s~384 tok/s~317 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 NVFP4?
At Q4_K_M, the download is about 4.63 GB. The full-precision Q8_0 version is 7.71 GB. The smallest option (Q2_K) is 3.28 GB.
- Which GPUs can run Gemma 4 12B IT NVFP4?
35 consumer GPUs can run Gemma 4 12B IT NVFP4 at Q4_K_M (5.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 12B IT NVFP4?
33 devices with unified memory can run Gemma 4 12B IT NVFP4 at Q4_K_M (5.7 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.