Huihui Qwen3 Coder Next Abliterated — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
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.
Doesn't fit a 24 GB GPU
Smallest consumer fit → Snapdragon X Elite Copilot+ PC · IQ2_S · 25.4 GB · ~53 tok/s
Macs from 32 GB (IQ2_M · ~70 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Huihui Qwen3 Coder Next 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/bartowski/huihui-ai_Qwen3-Coder-Next-abliterated-GGUF:Q4_K_MQ4_K_M · needs ~48.3 GB · 16K context
Download: huihui-ai_Qwen3-Coder-Next-abliterated-Q4_K_M.gguf48.56 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 96 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 A100 80GB (31 GB headroom) · Q4_K_M
- Generation speed
- ~227 tok/s
- generation speed
- Cost per 1M output tokens
- $1.39
- per 1M output tokens
How Much VRAM Does Huihui Qwen3 Coder Next Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 34.3 GB | 34.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 34.4 GB | |
| 35.3 GB | 35.6 GB | 3-bit small — 3-bit small quantization | Does not fit: needs 35.4 GB | |
| 39.2 GB | 39.5 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 39.3 GB | |
| 40.2 GB | 40.5 GB | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 40.3 GB | |
| 48.2 GB | 48.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 48.3 GB | |
| 57.2 GB | 57.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 57.3 GB | |
| 66.1 GB | 66.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 66.2 GB | |
| 80.1 GB | 80.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 80.2 GB |
Selected Q4_K_M
4-bit medium quantization48.3GBat 16K context
- Weights
- 47.8 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 24.3 GB more
Or .
Which Hardware 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 3.1 GB, bringing total usage to 51.3 GB. No consumer GPU has enough memory. 52 devices with unified memory can run Huihui Qwen3 Coder Next Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
32–40 GB
Q4_K_M · best fit IQ3_XXS (31.4 GB)
1 GPU · 2 Mac classes · 1 device family
RTX 5090~253Simulate output on RTX 5090M5 Max36 GB~70Simulate output on Mac Studio M5 Max (36 GB)M3 Pro*36 GB~42Simulate output on MacBook Pro 14" M3 Pro (36 GB)Orin~71Simulate output on NVIDIA Jetson AGX Orin 32GB64–80 GB
fits · 15.7 GB free · up to 256K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~73Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~66Simulate output on Mac Studio M5 Max (64 GB)Orin~49Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Huihui Qwen3 Coder Next Abliterated
Qwen 3 family · 79 models →Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| NVIDIA Nemotron 3.5 Bolt 74B A9B BF16NVIDIA | — | 54.2 GB | —needs > 24 GB | same size |
| Kolibri 1 BF16Aleph-Alpha | — | 22.5 GB | ~237 | same size |
| AliceAI Foundation 80B A3B Baseyandex | — | 49.9 GB | —needs > 24 GB | same size |
| Hunyuan A13B InstructTencent | — | 50.7 GB | —needs > 24 GB | same size |
| Kolibri 1Aleph-Alpha | — | 47.9 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 79.7B
- Active parameters
- 3.9B per token
- Architecture
- Qwen3NextForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-02-07
- License
- Apache 2.0
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 3.1 GB (51.3 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)
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 ≈ 3.5 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Huihui Qwen3 Coder Next Abliterated?
Yes, at IQ3_XXS (31.3 GB) or lower. Higher quantizations like IQ3_XS (33.3 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
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. Q4_K_L (49.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 24.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Huihui Qwen3 Coder Next Abliterated on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Huihui Qwen3 Coder Next Abliterated, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 48.2 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).
Can I run Huihui Qwen3 Coder Next Abliterated locally?
Yes — Huihui Qwen3 Coder Next Abliterated can run locally on consumer hardware. 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 = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.3 GB active ÷ (8000 × 0.65) = 0.45 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 3.09 ms = ~324 tok/s
Estimated speed at Q4_K_M (48.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Huihui 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 (IQ2_XS) is 23.90 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?
52 devices with unified memory can run Huihui Qwen3 Coder Next Abliterated at Q4_K_M (48.2 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.