MiniCPM5 1B
By OpenBMB · China
Updated 2026-08-28
Overview
A 1.1B-parameter base model from OpenBMB under Apache 2.0, pretrained bilingually on English and Chinese as a clean foundation for fine-tuning rather than out-of-the-box chat.
When to pick this model
- Starting point for custom fine-tuning on domain-specific data
- On-device or edge deployments with tight memory budgets
- English/Chinese bilingual base-model needs
- Research into small-model pretraining and distillation
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 0.6 GB |
| Q5_K_M | 0.8 GB |
| Q8_0 | 1.2 GB |
| FP16 (no quantization) | 2.2 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, MiniCPM5 1B fits an 8 GB consumer card at Q4_K_M (0.6 GB). Stepping up to Q8_0 nearly doubles the footprint to 1.2 GB, and unquantized FP16 weights take 2.2 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, MiniCPM5 1B needs roughly 1.4 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 45 tokens/sec on entry-level GPUs, on the order of 120 tokens/sec on a mid-range card, and up to 220 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches MiniCPM5 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 MiniCPM5 1B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (2.2 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (2.2 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (2.2 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (2.2 GB used) |
| 32 GB | RTX 5090 | FP16 (2.2 GB used) |
Which GPU should you buy to run MiniCPM5 1B?
To run MiniCPM5 1B locally at Q4, you need ~0.6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Extremely compact — under 1GB VRAM at Q4
- Apache 2.0 license allows unrestricted commercial use
- Clean pretrained base ideal for fine-tuning
- Suited to edge and on-device deployment
Limitations
- Not instruction-tuned — needs fine-tuning before conversational use
- Bilingual EN/ZH only; other languages not guaranteed
- Capabilities inherently limited at 1.1B parameters
Typical workloads
In our catalog grid, MiniCPM5 1B is filed under Custom Fine-Tuning, Edge/On-Device, Base Model — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense Transformer · 1.1B parameters · Llama-type architecture
Training: Bilingual EN/ZH pretraining (OpenBMB). Base model (non-SFT), ideal as a starting point for fine-tuning.
A clean, compact bilingual base model built for fine-tuning — not a ready-to-chat model out of the box.
Quick start
# HuggingFace : openbmb/MiniCPM5-1BOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does MiniCPM5 1B need?
At the recommended Q4_K_M quantization, MiniCPM5 1B needs about 0.6 GB of VRAM. Q8_0 takes 1.2 GB, and unquantized FP16 weights take 2.2 GB.
Can MiniCPM5 1B run without a GPU?
Yes — with roughly 1.4 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 MiniCPM5 1B support?
MiniCPM5 1B supports a 32k-token context window (32,768 tokens).
Can I use MiniCPM5 1B commercially?
Yes. MiniCPM5 1B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is MiniCPM5 1B on consumer hardware?
Our compatibility engine estimates on the order of 120 tokens/sec on a mid-range GPU and up to 220 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of MiniCPM5 1B should I download first?
Start with Q4_K_M (0.6 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.