NeuralDaredevil 8B Abliterated — Hardware Requirements & GPU Compatibility
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NeuralDaredevil 8B Abliterated is a 8.0B-parameter open language model from mlabonne. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 5.39 GB of VRAM — see which GPUs and Macs can run it below.
16.6K downloads
279 likesSpecifications
- Publisher
- mlabonne
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-05-27
- License
- Llama 3 Community
Get Started
HuggingFace
How Much VRAM Does NeuralDaredevil 8B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.0 GB | 4.8 GB | 3.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 4.9 GB | 3.51 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 5.3 GB | 3.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 5.4 GB | 4.02 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 6.2 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 7.1 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | 8 GB | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 9.4 GB | 8.03 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 NeuralDaredevil 8B Abliterated?
Q4_K_M · 5.4 GBNeuralDaredevil 8B Abliterated (Q4_K_M) requires 5.4 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 8K context window can add up to 0.8 GB, bringing total usage to 6.2 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 NeuralDaredevil 8B Abliterated?
Q4_K_M · 5.4 GB58 devices with unified memory can run NeuralDaredevil 8B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download NeuralDaredevil 8B 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 NeuralDaredevil 8B Abliterated need?
NeuralDaredevil 8B Abliterated requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at FP16. Full 8K context adds up to 0.8 GB (6.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context6.2 GB- What's the best quantization for NeuralDaredevil 8B Abliterated?
For NeuralDaredevil 8B Abliterated, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.0 GB.
VRAM requirement by quantization
Q2_K4.0 GBQ3_K_L4.7 GBQ4_K_M ★5.4 GBQ5_05.6 GBQ5_K_M6.3 GBFP1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run NeuralDaredevil 8B Abliterated on a Mac?
NeuralDaredevil 8B Abliterated requires at least 4.0 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 NeuralDaredevil 8B Abliterated locally?
Yes — NeuralDaredevil 8B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NeuralDaredevil 8B Abliterated?
At Q4_K_M, NeuralDaredevil 8B Abliterated can reach ~816 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 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 ÷ 5.4 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~965 tok/s~122 tok/s~965 tok/s~816 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of NeuralDaredevil 8B Abliterated?
At Q4_K_M, the download is about 4.82 GB. The full-precision FP16 version is 16.06 GB. The smallest option (Q2_K) is 3.41 GB.
- Which GPUs can run NeuralDaredevil 8B Abliterated?
50 consumer GPUs can run NeuralDaredevil 8B Abliterated at Q4_K_M (5.4 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 NeuralDaredevil 8B Abliterated?
59 devices with unified memory can run NeuralDaredevil 8B Abliterated at Q4_K_M (5.4 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.