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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~127 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~27 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Huihui Gemma 3n E4B IT Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/bartowski/huihui-ai_Huihui-gemma-3n-E4B-it-abliterated-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.8 GB free · 16K context
Download: huihui-ai_Huihui-gemma-3n-E4B-it-abliterated-Q4_K_M.gguf4.24 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (5 GB headroom) · Q4_K_M
- Generation speed
- ~45 tok/s
- generation speed
- Cost per 1M output tokens
- $0.46
- per 1M output tokens
How Much VRAM Does Huihui Gemma 3n E4B IT Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.8 GB | 6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~173 tok/s | |
| 3.9 GB | 6.1 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~169 tok/s | |
| 4.3 GB | 6.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~153 tok/s | |
| 4.4 GB | 6.6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~150 tok/s | |
| 5.2 GB | 7.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~127 tok/s | |
| 6.0 GB | 8.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~109 tok/s | |
| 6.9 GB | 9.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~95 tok/s | |
| 8.3 GB | 10.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~79 tok/s |
Selected Q4_K_M
4-bit medium quantization6.2GBat 16K context
- Weights
- 4.7 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.8 GB free · ~127 tok/s
≥50 tok/s needs ≥397 GB/s · RTX 5090 ~226, RTX 3090 Ti ~127
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Huihui Gemma 3n E4B IT Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.8 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~96Simulate output on RTX 3080RTX 2080 Ti~78Simulate output on RTX 2080 TiRTX 3070 Ti~77Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.8 GB free · up to 32K ctx
1 GPU
RX 7900 XT~93Simulate output on RX 7900 XT32–40 GB
fits · 25.8 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~226Simulate output on RTX 5090M5 Max36 GB~62Simulate output on Mac Studio M5 Max (36 GB)M532 GB~21Simulate output on MacBook Air 13" M5 (32 GB)Orin~26Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Huihui Gemma 3n E4B IT Abliterated
Gemma 3 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.7 GB (at full 33K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Huihui Gemma 3n E4B IT Abliterated on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Huihui Gemma 3n E4B IT Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Huihui Gemma 3n E4B IT Abliterated locally?
Yes — Huihui Gemma 3n E4B IT Abliterated can run locally on consumer hardware. 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 ~930 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 ÷ 5.16 × 0.65 = ~1008 tok/s
Estimated speed at Q4_K_M (5.2 GB)
Real-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?
52 consumer GPUs can run Huihui Gemma 3n E4B IT Abliterated at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Huihui Gemma 3n E4B IT Abliterated?
151 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.