Luciole 8B Base — Hardware Requirements & GPU Compatibility
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Luciole 8B Base is a 8.1B-parameter open language model from OpenLLM-France. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.58 GB of VRAM — see which GPUs and Macs can run it below.
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7 likesSpecifications
- Publisher
- OpenLLM-France
- Parameters
- 8.1B
- Architecture
- NemotronHForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,000
- Release Date
- 2026-02-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Luciole 8B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.2 GB | 31.6 GB | 3.43 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.7 GB | 32.1 GB | 3.94 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.6 GB | 33.1 GB | 4.85 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.5 GB | 34.0 GB | 5.75 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.4 GB | 34.9 GB | 6.66 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.8 GB | 36.3 GB | 8.08 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 16.9 GB | 44.4 GB | 16.15 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 Luciole 8B Base?
Q4_K_M · 5.6 GBLuciole 8B Base (Q4_K_M) requires 5.6 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 131K context window can add up to 27.5 GB, bringing total usage to 33.1 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 Luciole 8B Base?
Q4_K_M · 5.6 GB58 devices with unified memory can run Luciole 8B Base, 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 Luciole 8B Base need?
Luciole 8B Base requires 5.6 GB of VRAM at Q4_K_M, or 16.9 GB at BF16. Full 131K context adds up to 27.5 GB (33.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.1B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 28.3 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.6 GBQ4_K_M + full context33.1 GB- What's the best quantization for Luciole 8B Base?
For Luciole 8B Base, Q4_K_M (5.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
Q2_K4.2 GBQ4_K_M ★5.6 GBQ5_K_M6.5 GBQ6_K7.4 GBQ8_08.8 GBBF1616.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Luciole 8B Base on a Mac?
Luciole 8B Base requires at least 4.2 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 Luciole 8B Base locally?
Yes — Luciole 8B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 5.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Luciole 8B Base?
At Q4_K_M, Luciole 8B Base can reach ~789 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~117 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 ÷ 5.6 × 0.65 = ~932 tok/s
Estimated speed at Q4_K_M (5.6 GB)
~932 tok/s~117 tok/s~932 tok/s~789 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Luciole 8B Base?
At Q4_K_M, the download is about 4.85 GB. The full-precision BF16 version is 16.15 GB. The smallest option (Q2_K) is 3.43 GB.
- Which GPUs can run Luciole 8B Base?
50 consumer GPUs can run Luciole 8B Base at Q4_K_M (5.6 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 Luciole 8B Base?
59 devices with unified memory can run Luciole 8B Base at Q4_K_M (5.6 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.