Lucie 7B
By OpenLLM-France · France
Updated 2026-07-13
Overview
A French-sovereign 7B model from OpenLLM-France, backed by CNRS and LINAGORA, with a fully transparent and auditable training corpus.
When to pick this model
- EU public-sector projects with data sovereignty requirements
- French-language content generation and editorial work
- Research needing reproducible, openly documented training data
- Regulated environments demanding training-data provenance
- Demonstrating non-US-trained alternatives to stakeholders
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 16 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Lucie 7B fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 16 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Lucie 7B needs roughly 8 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 12 tokens/sec on entry-level GPUs, on the order of 35 tokens/sec on a mid-range card, and up to 90 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Lucie 7B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Lucie 7B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (16 GB used) |
Which GPU should you buy to run Lucie 7B?
To run Lucie 7B locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU (fr) | 54.2 |
| FrenchBench | 68 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Full European data sovereignty story
- Publicly available training corpus
- Strong formal French output
- Backed by CNRS and LINAGORA
Limitations
- 4k context is too short for modern RAG or long docs
- Weaker English than Mistral or Llama at the same size
- Smaller ecosystem of fine-tunes and tools
Typical workloads
In our catalog grid, Lucie 7B is filed under Formal French, Local Content — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: French-language output where quality matters.
Note the 4k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Llama-like · 32 layers · trained on Jean Zay (CNRS)
Training: OpenLLM-France project · 100% transparent corpus, high proportion of FR.
Pick it for sovereignty and provenance, not raw capability — the 4k context is the dealbreaker for most workloads.
Quick start
ollama run lucie:7bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does Lucie 7B need?
At the recommended Q4_K_M quantization, Lucie 7B needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.
Can Lucie 7B run without a GPU?
Yes — with roughly 8 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Lucie 7B support?
Lucie 7B supports a 4k-token context window (4,096 tokens).
Can I use Lucie 7B commercially?
Yes. Lucie 7B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Lucie 7B on consumer hardware?
Our compatibility engine estimates on the order of 35 tokens/sec on a mid-range GPU and up to 90 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Lucie 7B should I download first?
Start with Q4_K_M (5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.