Huihui Qwen3 Coder Next Abliterated — Hardware Requirements & GPU Compatibility
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Huihui Qwen3 Coder Next Abliterated is a 79.7B-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 48.21 GB of VRAM — see which GPUs and Macs can run it below.
1.5K downloads
65 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 79.7B
- Architecture
- Qwen3NextForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-02-07
- License
- Apache 2.0
Get Started
How Much VRAM Does Huihui Qwen3 Coder Next Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 34.3 GB | 47.0 GB | 33.86 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 39.2 GB | 52.0 GB | 38.84 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 48.2 GB | 61.0 GB | 47.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 57.2 GB | 70.0 GB | 56.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 66.1 GB | 78.9 GB | 65.73 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 80.1 GB | 92.9 GB | 79.67 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 159.8 GB | 172.5 GB | 159.35 GB | Brain floating point 16 — preferred for training |
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 Huihui Qwen3 Coder Next Abliterated?
Q4_K_M · 48.2 GBHuihui Qwen3 Coder Next Abliterated (Q4_K_M) requires 48.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 63+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 61.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Huihui Qwen3 Coder Next Abliterated?
Q4_K_M · 48.2 GB22 devices with unified memory can run Huihui Qwen3 Coder Next Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Huihui Qwen3 Coder Next Abliterated need?
Huihui Qwen3 Coder Next Abliterated requires 48.2 GB of VRAM at Q4_K_M, or 159.8 GB at BF16. Full 262K context adds up to 12.8 GB (61.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 79.7B × 4.8 bits ÷ 8 = 47.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M48.2 GBQ4_K_M + full context61.0 GB- Can NVIDIA GeForce RTX 5090 run Huihui Qwen3 Coder Next Abliterated?
No — Huihui Qwen3 Coder Next Abliterated requires at least 34.3 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Huihui Qwen3 Coder Next Abliterated?
For Huihui Qwen3 Coder Next Abliterated, Q4_K_M (48.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (57.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 34.3 GB.
VRAM requirement by quantization
Q2_K34.3 GBQ4_K_M ★48.2 GBQ5_K_M57.2 GBQ6_K66.1 GBQ8_080.1 GBBF16159.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui Qwen3 Coder Next Abliterated on a Mac?
Huihui Qwen3 Coder Next Abliterated requires at least 34.3 GB at Q2_K, 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 Qwen3 Coder Next Abliterated locally?
Yes — Huihui Qwen3 Coder Next Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 48.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui Qwen3 Coder Next Abliterated?
At Q4_K_M, Huihui Qwen3 Coder Next Abliterated can reach ~99 tok/s on AMD Instinct MI350X. 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 ÷ 48.2 × 0.65 = ~324 tok/s
Estimated speed at Q4_K_M (48.2 GB)
~324 tok/s~324 tok/s~296 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui Qwen3 Coder Next Abliterated?
At Q4_K_M, the download is about 47.80 GB. The full-precision BF16 version is 159.35 GB. The smallest option (Q2_K) is 33.86 GB.
- Which GPUs can run Huihui Qwen3 Coder Next Abliterated?
No single consumer GPU has enough VRAM to run Huihui Qwen3 Coder Next Abliterated at Q4_K_M (48.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Huihui Qwen3 Coder Next Abliterated?
23 devices with unified memory can run Huihui Qwen3 Coder Next Abliterated at Q4_K_M (48.2 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.