Huihui MoE 1.2B A0.6B — Hardware Requirements & GPU Compatibility
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Huihui MoE 1.2B A0.6B is a 1.3B-parameter open language model from huihui-ai. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 1.19 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 40K)Best on 24 GB (Q4_K_M · ~384 tok/s · 40K)Macs from 8 GB (Q4_K_M · ~65 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Huihui MoE 1.2B A0.6B
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
vllm serve huihui-ai/Huihui-MoE-1.2B-A0.6B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~221 tok/s
- generation speed
- Cost per 1M output tokens
- $0.077
- per 1M output tokens
How Much VRAM Does Huihui MoE 1.2B A0.6B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.0 GB | 2.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~417 tok/s | |
| 1.0 GB | 2.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~405 tok/s | |
| 1.2 GB | 3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 40K context · ~384 tok/s | |
| 1.3 GB | 3.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 40K context · ~366 tok/s | |
| 1.5 GB | 3.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 40K context · ~350 tok/s | |
| 1.7 GB | 3.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 40K context · ~326 tok/s | |
| 3.0 GB | 4.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 40K context · ~238 tok/s |
Selected Q4_K_M
4-bit medium quantization2.0GBat 16K context
- Weights
- 0.8 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.0 GB free · ~384 tok/s
≥50 tok/s needs ≥58 GB/s · RTX 5090 ~467, RTX 3090 Ti ~384
Which Hardware Can Run Huihui MoE 1.2B A0.6B?
Q4_K_M · 1.2 GBHuihui MoE 1.2B A0.6B (Q4_K_M) requires 1.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 41K context window can add up to 2.2 GB, bringing total usage to 3.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Huihui MoE 1.2B A0.6B, including NVIDIA DGX Spark, NVIDIA Jetson AGX Thor Developer Kit.
≤ 11 GB
≤ 11 GB tier →fits · 6.0 GB free · up to 40K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~339Simulate output on RTX 3080RTX 2080 Ti~304Simulate output on RTX 2080 TiRTX 3070 Ti~302Simulate output on RTX 3070 TiM3*8 GB~65Simulate output on iMac 24" M3 (8 GB)Orin Nano~83Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~49Simulate output on iPhone 1720 GB
fits · 18.0 GB free · up to 40K ctx
1 GPU
RX 7900 XT~142Simulate output on RX 7900 XT32–40 GB
fits · 30.0 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~467Simulate output on RTX 5090M5 Max36 GB~129Simulate output on Mac Studio M5 Max (36 GB)M532 GB~83Simulate output on MacBook Air 13" M5 (32 GB)Orin~147Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~93Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM V 4.6 ThinkingOpenBMB | — | 1.5 GB | ~580 | same size |
| Llama 3.2 1B InstructMeta | — | 2.3 GB | ~799 | same size |
| T5gemma L L Ul2 ITGoogle | — | 4.3 GB | ~240 | same size |
| Granite 3.0 1B A400m InstructIBM | — | 1.3 GB | ~524 | same size |
| EXAONE 4.0 1.2BLGAI-EXAONE | — | 2.1 GB | ~551 | same size |
Specifications
- Publisher
- huihui-ai
- Parameters
- 1.3B
- Active parameters
- 0.8B per token
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-06-16
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Huihui MoE 1.2B A0.6B need?
Huihui MoE 1.2B A0.6B requires 1.2 GB of VRAM at Q4_K_M, or 3.0 GB at BF16. Full 41K context adds up to 2.2 GB (3.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 4.8 bits ÷ 8 = 0.8 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 ≈ 2.6 GB (at full 41K context)
VRAM usage by quantization
What's the best quantization for Huihui MoE 1.2B A0.6B?
For Huihui MoE 1.2B A0.6B, Q4_K_M (1.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Huihui MoE 1.2B A0.6B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Huihui MoE 1.2B A0.6B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Huihui MoE 1.2B A0.6B locally?
Yes — Huihui MoE 1.2B A0.6B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Huihui MoE 1.2B A0.6B?
At Q4_K_M, Huihui MoE 1.2B A0.6B can reach ~174 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~384 tok/s. 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 → 0.7 GB active ÷ (8000 × 0.65) = 0.134 ms, plus 28 layers × 0.055 ms = 1.540 ms, so 1000 ÷ 1.674 ms = ~597 tok/s
Estimated speed at Q4_K_M (1.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Huihui MoE 1.2B A0.6B?
At Q4_K_M, the download is about 0.77 GB. The full-precision BF16 version is 2.56 GB. The smallest option (Q2_K) is 0.54 GB.
Which GPUs can run Huihui MoE 1.2B A0.6B?
52 consumer GPUs can run Huihui MoE 1.2B A0.6B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Huihui MoE 1.2B A0.6B?
163 devices with unified memory can run Huihui MoE 1.2B A0.6B at Q4_K_M (1.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.