Huihui Gemma 3n E4B IT Abliterated — Hardware Requirements & GPU Compatibility
Vision
Huihui Gemma 3n E4B IT Abliterated is a 7.8B-parameter open language model from huihui-ai in the Gemma 3 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 5.16 GB of VRAM — see which GPUs and Macs can run it below.
74 downloads
17 likesSpecifications
- Publisher
- huihui-ai
- Family
- Gemma 3
- Parameters
- 7.8B
- Architecture
- Gemma3nForConditionalGeneration
- Context Length
- 32,768 tokens
- Vocabulary Size
- 262,400
- Release Date
- 2025-07-07
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Huihui Gemma 3n E4B IT Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.8 GB | 6.0 GB | 3.34 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.9 GB | 6.1 GB | 3.43 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.3 GB | 6.5 GB | 3.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.4 GB | 6.6 GB | 3.92 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.2 GB | 7.4 GB | 4.71 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.0 GB | 8.2 GB | 5.59 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.9 GB | 9.1 GB | 6.48 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.3 GB | 10.5 GB | 7.85 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Huihui Gemma 3n E4B IT Abliterated?
Q4_K_M · 5.2 GBHuihui Gemma 3n E4B IT Abliterated (Q4_K_M) requires 5.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 2.2 GB, bringing total usage to 7.4 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Huihui Gemma 3n E4B IT Abliterated?
Q4_K_M · 5.2 GB58 devices with unified memory can run Huihui Gemma 3n E4B IT Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Huihui Gemma 3n E4B IT Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Huihui Gemma 3n E4B IT Abliterated need?
Huihui Gemma 3n E4B IT Abliterated requires 5.2 GB of VRAM at Q4_K_M, or 16.1 GB at BF16. Full 33K context adds up to 2.2 GB (7.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.8B × 4.8 bits ÷ 8 = 4.7 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M5.2 GBQ4_K_M + full context7.4 GB- What's the best quantization for Huihui Gemma 3n E4B IT Abliterated?
For Huihui Gemma 3n E4B IT Abliterated, Q4_K_M (5.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.3 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.7 GB.
VRAM requirement by quantization
IQ3_XS3.7 GBQ4_04.4 GBQ4_K_S4.9 GBQ4_K_M ★5.2 GBQ5_K_M6.0 GBBF1616.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui Gemma 3n E4B IT Abliterated on a Mac?
Huihui Gemma 3n E4B IT Abliterated requires at least 3.7 GB at IQ3_XS, 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 Huihui Gemma 3n E4B IT Abliterated locally?
Yes — Huihui Gemma 3n E4B IT Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 5.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui Gemma 3n E4B IT Abliterated?
At Q4_K_M, Huihui Gemma 3n E4B IT Abliterated can reach ~853 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~127 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 ÷ 5.2 × 0.65 = ~1008 tok/s
Estimated speed at Q4_K_M (5.2 GB)
~1008 tok/s~127 tok/s~1008 tok/s~853 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui Gemma 3n E4B IT Abliterated?
At Q4_K_M, the download is about 4.71 GB. The full-precision BF16 version is 15.70 GB. The smallest option (IQ3_XS) is 3.24 GB.
- Which GPUs can run Huihui Gemma 3n E4B IT Abliterated?
50 consumer GPUs can run Huihui Gemma 3n E4B IT Abliterated at Q4_K_M (5.2 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 Huihui Gemma 3n E4B IT Abliterated?
59 devices with unified memory can run Huihui Gemma 3n E4B IT Abliterated at Q4_K_M (5.2 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.