Pleias-RAG 1B
By PleIAs · France
Updated 2026-07-13
Overview
A 1.2B RAG-specialized model from PleIAs with built-in citation and grounding behavior. Beats most sub-4B small language models on HotPotQA.
When to pick this model
- You're deploying RAG on tight hardware budgets or edge devices
- You need clean citations and grounding from a small model
- You're handling structured Q&A where source attribution matters
- You want a defensible audit trail for regulated RAG deployments
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 0.8 GB |
| Q5_K_M | 1 GB |
| Q8_0 | 1.5 GB |
| FP16 (no quantization) | 2.5 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, Pleias-RAG 1B fits an 8 GB consumer card at Q4_K_M (0.8 GB). Stepping up to Q8_0 nearly doubles the footprint to 1.5 GB, and unquantized FP16 weights take 2.5 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Pleias-RAG 1B needs roughly 4 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 50 tokens/sec on entry-level GPUs, on the order of 150 tokens/sec on a mid-range card, and up to 280 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Pleias-RAG 1B 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 Pleias-RAG 1B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (2.5 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (2.5 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (2.5 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (2.5 GB used) |
| 32 GB | RTX 5090 | FP16 (2.5 GB used) |
Which GPU should you buy to run Pleias-RAG 1B?
To run Pleias-RAG 1B locally at Q4, you need ~0.8 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.
Strengths
- Built-in citation and grounding in RAG responses
- Outperforms most small language models under 4B on HotPotQA
- Runs on lightweight hardware
- Apache 2.0
Limitations
- Context window of only ~2K
- No official Ollama tag
- Specialized for RAG — not a general chat model
Typical workloads
In our catalog grid, Pleias-RAG 1B is filed under Lightweight RAG, Clean Citations — 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 2k-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: Dense 1.2B · fine-tuned for RAG with built-in citations/grounding
Training: Based on Pleias 1.2B.
The most efficient small open model for production RAG with citations.
Quick start
# HuggingFace : PleIAs/Pleias-RAG-1B (GGUF : PleIAs/Pleias-RAG-1B-gguf)Or 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 Pleias-RAG 1B need?
At the recommended Q4_K_M quantization, Pleias-RAG 1B needs about 0.8 GB of VRAM. Q8_0 takes 1.5 GB, and unquantized FP16 weights take 2.5 GB.
Can Pleias-RAG 1B run without a GPU?
Yes — with roughly 4 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 Pleias-RAG 1B support?
Pleias-RAG 1B supports a 2k-token context window (2,048 tokens).
Can I use Pleias-RAG 1B commercially?
Yes. Pleias-RAG 1B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Pleias-RAG 1B on consumer hardware?
Our compatibility engine estimates on the order of 150 tokens/sec on a mid-range GPU and up to 280 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Pleias-RAG 1B should I download first?
Start with Q4_K_M (0.8 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 FP16.