Gemma 3 12B IT Heretic v2 — Hardware Requirements & GPU Compatibility
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Gemma 3 12B IT Heretic v2 is a 12.2B-parameter open language model from diffusionmodels1254ani in the Gemma 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 8.37 GB of VRAM — see which GPUs and Macs can run it below.
1.0K downloads
2 likesSpecifications
- Publisher
- diffusionmodels1254ani
- Family
- Gemma 3
- Parameters
- 12.2B
- Architecture
- Gemma3ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,208
- Release Date
- 2026-05-12
- License
- Gemma Terms
Get Started
How Much VRAM Does Gemma 3 12B IT Heretic v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 6.2 GB | 53.8 GB | 5.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 7 GB | 54.6 GB | 5.94 GB | 3-bit medium quantization |
| Q4_K_S | 4.50 | 7.9 GB | 55.5 GB | 6.86 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 8.4 GB | 55.9 GB | 7.31 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 9.4 GB | 57 GB | 8.38 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 9.7 GB | 57.3 GB | 8.68 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 11.1 GB | 58.7 GB | 10.05 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 13.2 GB | 60.8 GB | 12.19 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 25.4 GB | 73.0 GB | 24.37 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 3 12B IT Heretic v2?
Q4_K_M · 8.4 GBGemma 3 12B IT Heretic v2 (Q4_K_M) requires 8.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 131K context window can add up to 47.6 GB, bringing total usage to 55.9 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 3 12B IT Heretic v2?
Q4_K_M · 8.4 GB27 devices with unified memory can run Gemma 3 12B IT Heretic v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3 12B IT Heretic v2 need?
Gemma 3 12B IT Heretic v2 requires 8.4 GB of VRAM at Q4_K_M, or 25.4 GB at BF16. Full 131K context adds up to 47.6 GB (55.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.2B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 48.6 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M8.4 GBQ4_K_M + full context55.9 GB- Can NVIDIA GeForce RTX 4090 run Gemma 3 12B IT Heretic v2?
Yes, at Q8_0 (13.2 GB) or lower. Higher quantizations like BF16 (25.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 3 12B IT Heretic v2?
For Gemma 3 12B IT Heretic v2, Q4_K_M (8.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (9.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.2 GB.
VRAM requirement by quantization
Q2_K6.2 GBQ4_K_S7.9 GBQ4_K_M ★8.4 GBQ5_K_S9.4 GBQ6_K11.1 GBBF1625.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 12B IT Heretic v2 on a Mac?
Gemma 3 12B IT Heretic v2 requires at least 6.2 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 Heretic v2 locally?
Yes — Gemma 3 12B IT Heretic v2 can run locally on consumer hardware. At Q4_K_M quantization it needs 8.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 12B IT Heretic v2?
At Q4_K_M, Gemma 3 12B IT Heretic v2 can reach ~348 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~78 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 ÷ 8.4 × 0.55 = ~348 tok/s
Estimated speed at Q4_K_M (8.4 GB)
~348 tok/s~78 tok/s~260 tok/s~215 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 Heretic v2?
At Q4_K_M, the download is about 7.31 GB. The full-precision BF16 version is 24.37 GB. The smallest option (Q2_K) is 5.18 GB.
- Which GPUs can run Gemma 3 12B IT Heretic v2?
28 consumer GPUs can run Gemma 3 12B IT Heretic v2 at Q4_K_M (8.4 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 3 12B IT Heretic v2?
27 devices with unified memory can run Gemma 3 12B IT Heretic v2 at Q4_K_M (8.4 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.