Huihui Qwen3.8 27B Abliterated — Hardware Requirements & GPU Compatibility
Vision
Huihui Qwen3.8 27B Abliterated is a 27.8B-parameter open language model from huihui-ai in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 56.31 GB of VRAM — see which GPUs and Macs can run it below.
65.7K downloads
361 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 3.8
- Parameters
- 27.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-08-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Huihui Qwen3.8 27B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 56.3 GB | 113.1 GB | 55.56 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 Qwen3.8 27B Abliterated?
BF16 · 56.3 GBHuihui Qwen3.8 27B Abliterated (BF16) requires 56.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 74+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 113.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Huihui Qwen3.8 27B Abliterated?
BF16 · 56.3 GB22 devices with unified memory can run Huihui Qwen3.8 27B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Where to Download Huihui Qwen3.8 27B Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Huihui Qwen3.8 27B Abliterated need?
Huihui Qwen3.8 27B Abliterated requires 56.3 GB of VRAM at BF16. Full 262K context adds up to 56.8 GB (113.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.8B × 16 bits ÷ 8 = 55.6 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.5 GB (at full 262K context)
VRAM usage by quantization
BF1656.3 GBBF16 + full context113.1 GB- Can NVIDIA GeForce RTX 5090 run Huihui Qwen3.8 27B Abliterated?
No — Huihui Qwen3.8 27B Abliterated requires at least 56.3 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Huihui Qwen3.8 27B Abliterated on a Mac?
Huihui Qwen3.8 27B Abliterated requires at least 56.3 GB at BF16, 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 Qwen3.8 27B Abliterated locally?
Yes — Huihui Qwen3.8 27B Abliterated can run locally on consumer hardware. At BF16 quantization it needs 56.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui Qwen3.8 27B Abliterated?
At BF16, Huihui Qwen3.8 27B Abliterated can reach ~85 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 ÷ 56.3 × 0.65 = ~92 tok/s
Estimated speed at BF16 (56.3 GB)
~92 tok/s~92 tok/s~85 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui Qwen3.8 27B Abliterated?
At BF16, the download is about 55.56 GB.
- Which GPUs can run Huihui Qwen3.8 27B Abliterated?
No single consumer GPU has enough VRAM to run Huihui Qwen3.8 27B Abliterated at BF16 (56.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Huihui Qwen3.8 27B Abliterated?
23 devices with unified memory can run Huihui Qwen3.8 27B Abliterated at BF16 (56.3 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.