Gemma 4 12B OBLITERATED — Hardware Requirements & GPU Compatibility
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Gemma 4 12B OBLITERATED is a 12.0B-parameter open language model from OBLITERATUS in the Gemma 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 8.23 GB of VRAM — see which GPUs and Macs can run it below.
108.4K downloads
409 likesSpecifications
- Publisher
- OBLITERATUS
- Family
- Gemma 4
- Parameters
- 12.0B
- Architecture
- Gemma4UnifiedForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-05
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 4 12B OBLITERATED Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 6.1 GB | 53.7 GB | 5.08 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 6.3 GB | 53.9 GB | 5.23 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 6.9 GB | 54.5 GB | 5.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 7.0 GB | 54.6 GB | 5.98 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 8.2 GB | 55.8 GB | 7.18 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 9.6 GB | 57.1 GB | 8.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.9 GB | 58.5 GB | 9.87 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 13.0 GB | 60.6 GB | 11.96 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 12B OBLITERATED?
Q4_K_M · 8.2 GBGemma 4 12B OBLITERATED (Q4_K_M) requires 8.2 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 55.8 GB. 39 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 4 12B OBLITERATED?
Q4_K_M · 8.2 GB49 devices with unified memory can run Gemma 4 12B OBLITERATED, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Gemma 4 12B OBLITERATED
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 OBLITERATED need?
Gemma 4 12B OBLITERATED requires 8.2 GB of VRAM at Q4_K_M, or 25.0 GB at BF16. Full 131K context adds up to 47.6 GB (55.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.0B × 4.8 bits ÷ 8 = 7.2 GB
KV Cache + Overhead ≈ 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_M8.2 GBQ4_K_M + full context55.8 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 12B OBLITERATED?
Yes, at Q8_0 (13.0 GB) or lower. Higher quantizations like BF16 (25.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 4 12B OBLITERATED?
For Gemma 4 12B OBLITERATED, Q4_K_M (8.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (9.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 4.3 GB.
VRAM requirement by quantization
IQ2_XXS4.3 GBIQ3_XS6.0 GBQ4_07.0 GBIQ4_NL7.8 GBQ4_K_M ★8.2 GBBF1625.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 12B OBLITERATED on a Mac?
Gemma 4 12B OBLITERATED requires at least 4.3 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 4 12B OBLITERATED locally?
Yes — Gemma 4 12B OBLITERATED can run locally on consumer hardware. At Q4_K_M quantization it needs 8.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 12B OBLITERATED?
At Q4_K_M, Gemma 4 12B OBLITERATED can reach ~535 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~80 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 ÷ 8.2 × 0.65 = ~632 tok/s
Estimated speed at Q4_K_M (8.2 GB)
~632 tok/s~80 tok/s~632 tok/s~535 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 OBLITERATED?
At Q4_K_M, the download is about 7.18 GB. The full-precision BF16 version is 23.92 GB. The smallest option (IQ2_XXS) is 3.29 GB.
- Which GPUs can run Gemma 4 12B OBLITERATED?
39 consumer GPUs can run Gemma 4 12B OBLITERATED at Q4_K_M (8.2 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 12B OBLITERATED?
52 devices with unified memory can run Gemma 4 12B OBLITERATED at Q4_K_M (8.2 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.