Huihui MoE 0.8B 2E — Hardware Requirements & GPU Compatibility
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26 downloads
9 likesSpecifications
- Publisher
- huihui-ai
- Parameters
- 860M
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-06-18
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Huihui MoE 0.8B 2E Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 2.1 GB | 4.4 GB | 1.72 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Huihui MoE 0.8B 2E?
BF16 · 2.1 GBHuihui MoE 0.8B 2E (BF16) requires 2.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 41K context window can add up to 2.2 GB, bringing total usage to 4.4 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Huihui MoE 0.8B 2E?
BF16 · 2.1 GB33 devices with unified memory can run Huihui MoE 0.8B 2E, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Huihui MoE 0.8B 2E need?
Huihui MoE 0.8B 2E requires 2.1 GB of VRAM at BF16. Full 41K context adds up to 2.2 GB (4.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 860M × 16 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.7 GB (at full 41K context)
VRAM usage by quantization
BF162.1 GBBF16 + full context4.4 GB- Can I run Huihui MoE 0.8B 2E on a Mac?
Huihui MoE 0.8B 2E requires at least 2.1 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 0.8B 2E locally?
Yes — Huihui MoE 0.8B 2E can run locally on consumer hardware. At BF16 quantization it needs 2.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui MoE 0.8B 2E?
At BF16, Huihui MoE 0.8B 2E can reach ~1362 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~306 tok/s. 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 ÷ 2.1 × 0.55 = ~1362 tok/s
Estimated speed at BF16 (2.1 GB)
AMD Instinct MI300X~1362 tok/sNVIDIA GeForce RTX 4090~306 tok/sNVIDIA H100 SXM~1018 tok/sAMD Instinct MI250X~842 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 0.8B 2E?
At BF16, the download is about 1.72 GB.
- Which GPUs can run Huihui MoE 0.8B 2E?
35 consumer GPUs can run Huihui MoE 0.8B 2E at BF16 (2.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Huihui MoE 0.8B 2E?
33 devices with unified memory can run Huihui MoE 0.8B 2E at BF16 (2.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.