Huihui LFM2.5 8B A1B Abliterated — Hardware Requirements & GPU Compatibility
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Huihui LFM2.5 8B A1B Abliterated is a 8.5B-parameter open language model from huihui-ai in the LFM2.5 family. It supports a context window of up to 128,000 tokens. At Q4_K_M it needs about 5.48 GB of VRAM — see which GPUs and Macs can run it below.
171 downloads
8 likesSpecifications
- Publisher
- huihui-ai
- Family
- LFM2.5
- Parameters
- 8.5B
- Architecture
- Lfm2MoeForCausalLM
- Context Length
- 128,000 tokens
- Vocabulary Size
- 128,000
- Release Date
- 2026-06-01
- License
- Other
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HuggingFace
How Much VRAM Does Huihui LFM2.5 8B A1B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4 GB | 10.2 GB | 3.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.5 GB | 10.7 GB | 4.13 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.5 GB | 11.7 GB | 5.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.4 GB | 12.6 GB | 6.03 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.4 GB | 13.6 GB | 6.99 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.9 GB | 15.1 GB | 8.47 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 17.3 GB | 23.5 GB | 16.94 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 LFM2.5 8B A1B Abliterated?
Q4_K_M · 5.5 GBHuihui LFM2.5 8B A1B Abliterated (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 128K context window can add up to 6.2 GB, bringing total usage to 11.7 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Huihui LFM2.5 8B A1B Abliterated?
Q4_K_M · 5.5 GB58 devices with unified memory can run Huihui LFM2.5 8B A1B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Huihui LFM2.5 8B A1B Abliterated need?
Huihui LFM2.5 8B A1B Abliterated requires 5.5 GB of VRAM at Q4_K_M, or 17.3 GB at BF16. Full 128K context adds up to 6.2 GB (11.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 4.8 bits ÷ 8 = 5.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.6 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M5.5 GBQ4_K_M + full context11.7 GB- What's the best quantization for Huihui LFM2.5 8B A1B Abliterated?
For Huihui LFM2.5 8B A1B Abliterated, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4 GB.
VRAM requirement by quantization
Q2_K4.0 GBQ4_K_M ★5.5 GBQ5_K_M6.4 GBQ6_K7.4 GBQ8_08.9 GBBF1617.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui LFM2.5 8B A1B Abliterated on a Mac?
Huihui LFM2.5 8B A1B Abliterated requires at least 4 GB at Q2_K, 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 LFM2.5 8B A1B Abliterated locally?
Yes — Huihui LFM2.5 8B A1B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui LFM2.5 8B A1B Abliterated?
At Q4_K_M, Huihui LFM2.5 8B A1B Abliterated can reach ~803 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~120 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 ÷ 5.5 × 0.65 = ~949 tok/s
Estimated speed at Q4_K_M (5.5 GB)
~949 tok/s~120 tok/s~949 tok/s~803 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui LFM2.5 8B A1B Abliterated?
At Q4_K_M, the download is about 5.08 GB. The full-precision BF16 version is 16.94 GB. The smallest option (Q2_K) is 3.60 GB.
- Which GPUs can run Huihui LFM2.5 8B A1B Abliterated?
50 consumer GPUs can run Huihui LFM2.5 8B A1B Abliterated at Q4_K_M (5.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Huihui LFM2.5 8B A1B Abliterated?
59 devices with unified memory can run Huihui LFM2.5 8B A1B Abliterated at Q4_K_M (5.5 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.