Huihui MiniCPM5 1B Abliterated — Hardware Requirements & GPU Compatibility
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Huihui MiniCPM5 1B Abliterated is a 1.1B-parameter open language model from huihui-ai in the MiniCPM family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 0.99 GB of VRAM — see which GPUs and Macs can run it below.
124 downloads
6 likesSpecifications
- Publisher
- huihui-ai
- Family
- MiniCPM
- Parameters
- 1.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 130,560
- Release Date
- 2026-06-02
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Huihui MiniCPM5 1B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | 3.2 GB | 0.46 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.8 GB | 3.2 GB | 0.47 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.9 GB | 3.2 GB | 0.53 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.9 GB | 3.3 GB | 0.54 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.0 GB | 3.4 GB | 0.65 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.1 GB | 3.5 GB | 0.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.2 GB | 3.6 GB | 0.89 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.4 GB | 3.8 GB | 1.08 GB | 8-bit quantization, near-lossless |
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 MiniCPM5 1B Abliterated?
Q4_K_M · 1.0 GBHuihui MiniCPM5 1B Abliterated (Q4_K_M) requires 1.0 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 131K context window can add up to 2.4 GB, bringing total usage to 3.4 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Huihui MiniCPM5 1B Abliterated?
Q4_K_M · 1.0 GB59 devices with unified memory can run Huihui MiniCPM5 1B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Huihui MiniCPM5 1B Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Huihui MiniCPM5 1B Abliterated need?
Huihui MiniCPM5 1B Abliterated requires 1.0 GB of VRAM at Q4_K_M, or 2.5 GB at BF16. Full 131K context adds up to 2.4 GB (3.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M1.0 GBQ4_K_M + full context3.4 GB- What's the best quantization for Huihui MiniCPM5 1B Abliterated?
For Huihui MiniCPM5 1B Abliterated, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.6 GB.
VRAM requirement by quantization
IQ2_XXS0.6 GBIQ3_XS0.8 GBQ4_00.9 GBIQ4_NL0.9 GBQ4_K_M ★1.0 GBBF162.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui MiniCPM5 1B Abliterated on a Mac?
Huihui MiniCPM5 1B Abliterated requires at least 0.6 GB at IQ2_XXS, 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 MiniCPM5 1B Abliterated locally?
Yes — Huihui MiniCPM5 1B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui MiniCPM5 1B Abliterated?
At Q4_K_M, Huihui MiniCPM5 1B Abliterated can reach ~4444 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~662 tok/s. 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 ÷ 1.0 × 0.65 = ~5253 tok/s
Estimated speed at Q4_K_M (1.0 GB)
~5253 tok/s~662 tok/s~5253 tok/s~4444 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui MiniCPM5 1B Abliterated?
At Q4_K_M, the download is about 0.65 GB. The full-precision BF16 version is 2.16 GB. The smallest option (IQ2_XXS) is 0.30 GB.
- Which GPUs can run Huihui MiniCPM5 1B Abliterated?
50 consumer GPUs can run Huihui MiniCPM5 1B Abliterated at Q4_K_M (1.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Huihui MiniCPM5 1B Abliterated?
59 devices with unified memory can run Huihui MiniCPM5 1B Abliterated at Q4_K_M (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.