Gemma 3 4B IT Heretic Uncensored Abliterated Balanced — Hardware Requirements & GPU Compatibility
Vision
Gemma 3 4B IT Heretic Uncensored Abliterated Balanced is a 4.3B-parameter open language model from DavidAU in the Gemma 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 3.24 GB of VRAM — see which GPUs and Macs can run it below.
14 downloads
6 likesSpecifications
- Publisher
- DavidAU
- Family
- Gemma 3
- Parameters
- 4.3B
- Architecture
- Gemma3ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,208
- Release Date
- 2025-11-22
Get Started
How Much VRAM Does Gemma 3 4B IT Heretic Uncensored Abliterated Balanced Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.5 GB | 24.9 GB | 1.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.5 GB | 25 GB | 1.88 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.8 GB | 25.2 GB | 2.10 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.8 GB | 25.3 GB | 2.15 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.2 GB | 25.7 GB | 2.58 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.7 GB | 26.2 GB | 3.06 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.2 GB | 26.7 GB | 3.55 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 5.0 GB | 27.4 GB | 4.30 GB | 8-bit quantization, near-lossless |
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 4B IT Heretic Uncensored Abliterated Balanced?
Q4_K_M · 3.2 GBGemma 3 4B IT Heretic Uncensored Abliterated Balanced (Q4_K_M) requires 3.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 131K context window can add up to 22.5 GB, bringing total usage to 25.7 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced?
Q4_K_M · 3.2 GB59 devices with unified memory can run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gemma 3 4B IT Heretic Uncensored Abliterated Balanced
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 3 4B IT Heretic Uncensored Abliterated Balanced need?
Gemma 3 4B IT Heretic Uncensored Abliterated Balanced requires 3.2 GB of VRAM at Q4_K_M, or 9.3 GB at BF16. Full 131K context adds up to 22.5 GB (25.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.3B × 4.8 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 23.1 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M3.2 GBQ4_K_M + full context25.7 GB- What's the best quantization for Gemma 3 4B IT Heretic Uncensored Abliterated Balanced?
For Gemma 3 4B IT Heretic Uncensored Abliterated Balanced, Q4_K_M (3.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.8 GB.
VRAM requirement by quantization
IQ2_XXS1.8 GBIQ3_XS2.4 GBQ4_02.8 GBIQ4_NL3.1 GBQ4_K_M ★3.2 GBBF169.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced on a Mac?
Gemma 3 4B IT Heretic Uncensored Abliterated Balanced requires at least 1.8 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 4B IT Heretic Uncensored Abliterated Balanced locally?
Yes — Gemma 3 4B IT Heretic Uncensored Abliterated Balanced can run locally on consumer hardware. At Q4_K_M quantization it needs 3.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 4B IT Heretic Uncensored Abliterated Balanced?
At Q4_K_M, Gemma 3 4B IT Heretic Uncensored Abliterated Balanced can reach ~1358 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~202 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 ÷ 3.2 × 0.65 = ~1605 tok/s
Estimated speed at Q4_K_M (3.2 GB)
~1605 tok/s~202 tok/s~1605 tok/s~1358 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 4B IT Heretic Uncensored Abliterated Balanced?
At Q4_K_M, the download is about 2.58 GB. The full-precision BF16 version is 8.60 GB. The smallest option (IQ2_XXS) is 1.18 GB.
- Which GPUs can run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced?
50 consumer GPUs can run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced at Q4_K_M (3.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced?
59 devices with unified memory can run Gemma 3 4B IT Heretic Uncensored Abliterated Balanced at Q4_K_M (3.2 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.