LLMs ·

MiniCPM5 2B

Parameters
2.5B
Architecture
Dense
Max context
128K tokens
License
apache-2.0
Released
2026-09

How to run it

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/openbmb/MiniCPM5-2B-GGUF (opens in a new tab)

ollama run hf.co/openbmb/MiniCPM5-2B-GGUF

Original weights · Hugging Face

Full-precision files from OpenBMB, for fine-tuning, vLLM, Transformers.

huggingface.co/openbmb/MiniCPM5-2B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from OpenBMB. The weights themselves download from Hugging Face.

github.com/OpenBMB/MiniCPM (opens in a new tab)

OpenBMB's official MiniCPM repo, with deployment guides for the MiniCPM5 series.

git clone https://github.com/OpenBMB/MiniCPM

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

Apps that run MiniCPM5 2B

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

LM Studio

Runs the MLX build, typically 35–90% faster than GGUF for a model with 2.5B active parameters on Apple silicon.

Also good:

Windows PC, NVIDIA GPU

LM Studio

Point-and-click: search, download and chat in one window.

Also good:

Windows PC, AMD Radeon

Lemonade

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

Also good:

Linux, NVIDIA GPU

LM Studio

Point-and-click: search, download and chat in one window.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App Type Opens it as Systems Engine Start
LM Studio Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX Open in LM Studio
Ollama CLI + API GGUF from Hugging Face macOSWindowsLinux llama.cpp, MLX 
ollama run hf.co/openbmb/MiniCPM5-2B-GGUF:Q4_K_M
llama.cpp CLI + API GGUF macOSWindowsLinux llama.cpp 
llama-server -hf openbmb/MiniCPM5-2B-GGUF:Q4_K_M
Jan Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX Open in Jan
MLX LM CLI + API MLX macOS MLX 
mlx_lm.chat --model mlx-community/MiniCPM5-2B-mlx-4Bit
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve openbmb/MiniCPM5-2B
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path openbmb/MiniCPM5-2B
Docker Model Runner CLI + API GGUF macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/openbmb/MiniCPM5-2B-GGUF:Q4_K_M
Lemonade Desktop app GGUF macOSWindowsLinux llama.cpp, ONNX Runtime GenAI 
lemonade pull openbmb/MiniCPM5-2B-GGUF:Q4_K_M
Unsloth Desktop Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX, stable-diffusion.cpp Open in Unsloth Desktop
Atomic Chat Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX-VLM Open in Atomic Chat
Pi Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
Hermes Agent Assistant GGUF or MLX macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenClaw Assistant GGUF or MLX macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenCode Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Codex CLI Coding agent GGUF or MLX macOSWSLWindowsLinux uses Ollama or any OpenAI-compatible server Point it at a llama.cpp or MLX LM server
OpenHands Coding agent GGUF or MLX macOSWSLWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Zed Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Cline Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open Interpreter Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Goose Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Aider Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Crush Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Qwen Code Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Kilo Code Coding agent GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open WebUI Assistant GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Khoj Assistant GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Letta Assistant GGUF or MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
KoboldCpp Desktop app GGUF macOSWindowsLinux llama.cpp Get KoboldCpp
Text Generation WebUI Desktop app GGUF macOSWindowsLinux llama.cpp, Transformers, ExLlamaV3 Get Text Generation WebUI
llamafile CLI + API GGUF macOSWindowsLinux llama.cpp Get llamafile
Msty Studio Desktop app GGUF or MLX macOSWindowsLinux Ollama, llama.cpp, MLX Open in Msty Studio
Llama (macOS) Desktop app GGUF macOS llama.cpp Open in Llama (macOS)
RecurseChat Desktop app GGUF macOS llama.cpp Open in RecurseChat
Inferencer Desktop app MLX macOS MLX Get Inferencer
node-llama-cpp Library GGUF macOSWindowsLinux llama.cpp Get node-llama-cpp
exo Server MLX macOSLinux MLX Get exo
Continue Coding agent GGUF or MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
SillyTavern Assistant GGUF or MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
LibreChat Assistant GGUF or MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Cherry Studio Assistant GGUF or MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Chatbox Assistant GGUF or MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
PrivateGPT Assistant GGUF or MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
PocketPal AI Mobile app GGUF iOSAndroid llama.cpp App Store Google Play
ChatterUI Mobile app GGUF Android llama.cpp Download the APK
LLM Farm Mobile app GGUF iOS llama.cpp Not on the App Store or TestFlight for now (checked 2026-11-09).

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 1.0 GB 2.0 GB 3.0 GB GGUF files (opens in a new tab)
Q3_K_M Low 1.2 GB 2.2 GB 3.2 GB GGUF files (opens in a new tab)
Q4_K_M Good 1.5 GB 2.5 GB 3.5 GB GGUF (opens in a new tab)
Q5_K_M High 1.8 GB 2.7 GB 3.8 GB GGUF files (opens in a new tab)
Q6_K High 2.1 GB 3.0 GB 4.1 GB GGUF files (opens in a new tab)
Q8_0 Very high 2.7 GB 3.6 GB 4.7 GB GGUF (opens in a new tab)
F16 Full 5.0 GB 6.0 GB 7.0 GB GGUF (opens in a new tab) Original weights (opens in a new tab)

Best hardware for MiniCPM5 2B

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at Q6_K.

Quality Hardware Quant Memory Speed Runs
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GBNVIDIA GeForce RTX 3060 12GB F16 6.6 GB / 12.0 GB 41.1 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPERNVIDIA GeForce RTX 4070 SUPER F16 6.6 GB / 12.0 GB 57.6 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060NVIDIA GeForce RTX 4060 Q8_0 4.0 GB / 8.0 GB 67.5 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Sapphire NITRO+ AMD Radeon RX 9070 XT OCAMD Radeon RX 9070 XT F16 6.8 GB / 16.0 GB 63.0 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPERNVIDIA GeForce RTX 4070 Ti SUPER F16 6.8 GB / 16.0 GB 76.8 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPERNVIDIA GeForce RTX 4080 SUPER F16 6.8 GB / 16.0 GB 84.1 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC EditionNVIDIA GeForce RTX 5060 Ti 16GB F16 6.8 GB / 16.0 GB 51.2 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. ASUS TUF Gaming GeForce RTX 5070 Ti 16GB GDDR7 OC EditionNVIDIA GeForce RTX 5070 Ti F16 6.8 GB / 16.0 GB 102 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Founders EditionNVIDIA GeForce RTX 5080 F16 6.8 GB / 16.0 GB 110 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 Limited EditionIntel Arc B580 Q8_0 4.2 GB / 12.0 GB 86.4 tok/s Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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