Granite 4.0 H-Tiny 7B-A1BvsLucie 7B
Complete comparison between Granite 4.0 H-Tiny 7B-A1B (7B parameters, IBM) and Lucie 7B (7B, OpenLLM-France). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Granite 4.0 H-Tiny 7B-A1B | Lucie 7B |
|---|---|---|
| Settings | 7B | 7B |
| Family | Granite | Lucie |
| Author | IBM | OpenLLM-France |
| Origin | US | FR |
| License | Apache 2.0 | Apache 2.0 |
| Context | 128,000 tokens | 4,096 tokens |
| Output | October 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 | Granite 4.0 H-Tiny 7B-A1B | Lucie 7B |
|---|---|---|
| Q4_K_M (lightweight) | 4 GB | 5 GB |
| Q5_K_M (balanced) | 5 GB | 6 GB |
| Q8 (near-lossless) | 7 GB | 9 GB |
| FP16 (max quality) | 14 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 | Granite 4.0 H-Tiny 7B-A1B | Lucie 7B |
|---|---|---|
| RTX 4090 (24 GB) | 350 tok/s · FP16 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 180 tok/s · FP16 | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 60 tok/s · Q8 | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 180 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.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Refunded within 30 days
Strengths and weaknesses
Granite 4.0 H-Tiny 7B-A1B
Hybrid 7B/1B active. Extremely low compute cost. Edge/embedded.
- Very low compute cost
- Apache 2.0
- Efficient long context
- Below 3B dense quality for certain tasks
Installation
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
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Granite 4.0 H-Tiny 7B-A1B or Lucie 7B?
On an RTX 4090, Granite 4.0 H-Tiny 7B-A1B runs at FP16 (~350 tok/s), Lucie 7B in FP16 (~90 tok/s). In pure throughput, Granite 4.0 H-Tiny 7B-A1B wins. See the configurator to test your exact GPU.
Granite 4.0 H-Tiny 7B-A1B or Lucie 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?
In Q4_K_M, Granite 4.0 H-Tiny 7B-A1B fits in 4 GB versus 5 GB for its rival — a 1 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Granite 4.0 H-Tiny 7B-A1B is under Apache 2.0 — free for commercial use. Lucie 7B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Granite 4.0 H-Tiny 7B-A1B (7B). More capable: Granite 4.0 H-Tiny 7B-A1B (7B). If you're unsure, run it 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.