Lucie 7BvsDeepSeek R1 Distill 7B
Complete comparison between Lucie 7B (7B parameters, OpenLLM-France) and DeepSeek R1 Distill 7B (7B, DeepSeek). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from the catalog data—nothing is copied and pasted between pages.
In brief
| Feature | Lucie 7B | DeepSeek R1 Distill 7B |
|---|---|---|
| Settings | 7B | 7B |
| Family | Lucie | DeepSeek |
| Author | OpenLLM-France | DeepSeek |
| Origin | FR | CN |
| License | Apache 2.0 | MIT |
| Context | 4,096 tokens | 32 768 tokens |
| Output | January 2025 | January 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Lucie 7B | DeepSeek R1 Distill 7B |
|---|---|---|
| Q4_K_M (lightweight) | 5 GB | 5 GB |
| Q5_K_M (balanced) | 6 GB | 6 GB |
| Q8 (near-lossless) | 9 GB | 9 GB |
| FP16 (max quality) | 16 GB | 16 GB |
| CPU-only RAM | 8 GB | 8 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Lucie 7B | DeepSeek R1 Distill 7B |
|---|---|---|
| RTX 4090 (24 GB) | 90 tok/s · FP16 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 35 tok/s · FP16 | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 12 tok/s · Q8 | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 35 tok/s · FP16 | 35 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Lucie 7B
Sovereign French-language LLM, trained on French corpora.
- Full French sovereignty
- Public training data
- Excellent in formal French
- Public support (CNRS, LINAGORA)
- 4k context only
- Less versatile than Mistral in English
Installation
DeepSeek R1 Distill 7B
Distilled chain-of-thought reasoning. Impressive at math.
- Explicit CoT reasoning in 7B
- MIT License
- 32k ctx
- Surprisingly strong on AIME/MATH for its size
- Very verbose (thinking tokens)
- Worse than R1 32B at complex reasoning
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Lucie 7B or DeepSeek R1 Distill 7B?
On an RTX 4090, Lucie 7B runs at FP16 (~90 tok/s), DeepSeek R1 Distill 7B in FP16 (~90 tok/s). In pure throughput, DeepSeek R1 Distill 7B wins. See the configurator to test your exact GPU.
Lucie 7B or DeepSeek R1 Distill 7B: which is better in French?
For formal French (writing, summarization, translation), Lucie 7B has the edge—trained on a dense French-language corpus. The other remains usable, but for less demanding use cases.
Which uses the least VRAM?
Both require the same VRAM in Q4 (5 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Lucie 7B is under Apache 2.0 — free for commercial use. DeepSeek R1 Distill 7B is also free under the MIT license. For SaaS, favor Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Lucie 7B (7B). More capable: Lucie 7B (7B). If you’re unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.