Huihui Qwen3 4B Instruct 2507 Abliterated — Hardware Requirements & GPU Compatibility
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Huihui Qwen3 4B Instruct 2507 Abliterated is a 4.0B-parameter open language model from huihui-ai in the Qwen 3 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.90 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 · ~226 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Huihui Qwen3 4B Instruct 2507 Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Huihui-Qwen3-4B-Instruct-2507-abliterated-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.8 GB free · 16K context
Download: Huihui-Qwen3-4B-Instruct-2507-abliterated.Q4_K_M.gguf2.50 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~81 tok/s
- generation speed
- Cost per 1M output tokens
- $0.20
- per 1M output tokens
How Much VRAM Does Huihui Qwen3 4B Instruct 2507 Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.2 GB | 5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~298 tok/s | |
| 2.3 GB | 5.1 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~291 tok/s | |
| 2.5 GB | 5.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~267 tok/s | |
| 2.5 GB | 5.4 GB | 3-bit large — 3-bit large quantization | Fits: 128K context · ~257 tok/s | |
| 2.9 GB | 5.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~226 tok/s | |
| 3.4 GB | 6.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~196 tok/s | |
| 3.8 GB | 6.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~172 tok/s | |
| 4.5 GB | 7.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~145 tok/s |
Selected Q4_K_M
4-bit medium quantization4.2GBat 16K context
- Weights
- 2.4 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.8 GB free · ~226 tok/s
≥50 tok/s needs ≥223 GB/s · RTX 5090 ~402, RTX 3090 Ti ~226
Which Hardware Can Run Huihui Qwen3 4B Instruct 2507 Abliterated?
Q4_K_M · 2.9 GBHuihui Qwen3 4B Instruct 2507 Abliterated (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 26.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Huihui Qwen3 4B Instruct 2507 Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.8 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~170Simulate output on RTX 3080RTX 2080 Ti~138Simulate output on RTX 2080 TiRTX 3070 Ti~136Simulate output on RTX 3070 TiM3*8 GB~25Simulate output on iMac 24" M3 (8 GB)Orin Nano~23Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~17Simulate output on iPhone 1720 GB
fits · 15.8 GB free · up to 128K ctx
1 GPU
RX 7900 XT~166Simulate output on RX 7900 XT32–40 GB
fits · 27.8 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~402Simulate output on RTX 5090M5 Max36 GB~111Simulate output on Mac Studio M5 Max (36 GB)M532 GB~37Simulate output on MacBook Air 13" M5 (32 GB)Orin~46Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~30Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Huihui Qwen3 4B Instruct 2507 Abliterated
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Spark X2.6 4BXHToken | — | 4.3 GB | ~221 | same size |
| NVIDIA Nemotron Labs Dory1.0 4B BF16NVIDIA | — | 5.7 GB | ~214 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Translategemma 4B ITGoogle | — | 5 GB | ~231 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
Specifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 4.0B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-08-07
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Huihui Qwen3 4B Instruct 2507 Abliterated need?
Huihui Qwen3 4B Instruct 2507 Abliterated requires 2.9 GB of VRAM at Q4_K_M, or 8.5 GB at BF16. Full 262K context adds up to 24.0 GB (26.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 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 ≈ 24.5 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Huihui Qwen3 4B Instruct 2507 Abliterated?
For Huihui Qwen3 4B Instruct 2507 Abliterated, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Huihui Qwen3 4B Instruct 2507 Abliterated on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Huihui Qwen3 4B Instruct 2507 Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Huihui Qwen3 4B Instruct 2507 Abliterated locally?
Yes — Huihui Qwen3 4B Instruct 2507 Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Huihui Qwen3 4B Instruct 2507 Abliterated?
At Q4_K_M, Huihui Qwen3 4B Instruct 2507 Abliterated can reach ~1655 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~226 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 ÷ 2.9 × 0.65 = ~1793 tok/s
Estimated speed at Q4_K_M (2.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Huihui Qwen3 4B Instruct 2507 Abliterated?
At Q4_K_M, the download is about 2.41 GB. The full-precision BF16 version is 8.04 GB. The smallest option (Q2_K) is 1.71 GB.
Which GPUs can run Huihui Qwen3 4B Instruct 2507 Abliterated?
52 consumer GPUs can run Huihui Qwen3 4B Instruct 2507 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 Qwen3 4B Instruct 2507 Abliterated?
163 devices with unified memory can run Huihui Qwen3 4B Instruct 2507 Abliterated at Q4_K_M (2.9 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.