Huihui MoE 23B A4B Abliterated — Hardware Requirements & GPU Compatibility
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239 downloads
4 likesSpecifications
- Publisher
- huihui-ai
- Parameters
- 23.2B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-06-21
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Huihui MoE 23B A4B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 47.0 GB | 50.5 GB | 46.48 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Huihui MoE 23B A4B Abliterated?
BF16 · 47.0 GBHuihui MoE 23B A4B Abliterated (BF16) requires 47.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 62+ GB is recommended. Using the full 41K context window can add up to 3.6 GB, bringing total usage to 50.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Huihui MoE 23B A4B Abliterated?
BF16 · 47.0 GB11 devices with unified memory can run Huihui MoE 23B A4B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Huihui MoE 23B A4B Abliterated need?
Huihui MoE 23B A4B Abliterated requires 47.0 GB of VRAM at BF16. Full 41K context adds up to 3.6 GB (50.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 23.2B × 16 bits ÷ 8 = 46.5 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4 GB (at full 41K context)
VRAM usage by quantization
BF1647.0 GBBF16 + full context50.5 GB- Can NVIDIA GeForce RTX 5090 run Huihui MoE 23B A4B Abliterated?
No — Huihui MoE 23B A4B Abliterated requires at least 47.0 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Huihui MoE 23B A4B Abliterated on a Mac?
Huihui MoE 23B A4B Abliterated requires at least 47.0 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 MoE 23B A4B Abliterated locally?
Yes — Huihui MoE 23B A4B Abliterated can run locally on consumer hardware. At BF16 quantization it needs 47.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui MoE 23B A4B Abliterated?
At BF16, Huihui MoE 23B A4B Abliterated can reach ~62 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 47.0 × 0.55 = ~62 tok/s
Estimated speed at BF16 (47.0 GB)
AMD Instinct MI300X~62 tok/sNVIDIA H100 SXM~46 tok/sAMD Instinct MI250X~38 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui MoE 23B A4B Abliterated?
At BF16, the download is about 46.48 GB.
- Which GPUs can run Huihui MoE 23B A4B Abliterated?
No single consumer GPU has enough VRAM to run Huihui MoE 23B A4B Abliterated at BF16 (47.0 GB). Multi-GPU or professional hardware is required.
- Which devices can run Huihui MoE 23B A4B Abliterated?
11 devices with unified memory can run Huihui MoE 23B A4B Abliterated at BF16 (47.0 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.