readygpu

MiniCPM5 2B

2.5B parameters · 128k context · Apache License 2.0 · released 2026-09-06

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In short

MiniCPM5 2B is an open model from openbmb with 2.5B parameters. At mlx-4bit, MiniCPM5 2B needs about 2 GB of memory at 8k context and 8 GB at 128k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in Canada that runs MiniCPM5 2B fully in memory at 128k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 27 tok/s (estimated).

Updated . Speeds are estimates; how they are worked out.

What to run it on in Canada

Fully in memory at 128k context, priced at retailers today.

All hardware that runs it

Everything we price, best value first. Two cards split the model by layers.

HardwareQuantFitsDecodePricetok/s per 1,000
NVIDIA GeForce RTX 3060 12GBQ8_0fits27 tok/s$49753.9
Intel Arc B580Q8_0fits27 tok/s$56048.9
Intel Arc A770 16GBQ8_0fits34 tok/s$65551.4
NVIDIA GeForce RTX 3080 10GBQ8_0fits57 tok/s$76973.7
AMD Radeon RX 9060 XT 16GBQ8_0fits21 tok/s$80026.5
AMD Radeon RX 9070 XTQ8_0fits42 tok/s$1,05040.3
Intel Arc Pro B60Q8_0fits27 tok/s$1,06025.8
NVIDIA GeForce RTX 5060 Ti 16GBQ8_0fits33 tok/s$1,12029.8
NVIDIA GeForce RTX 5070Q8_0fits50 tok/s$1,18142.4
AMD Radeon RX 7800 XTQ8_0fits41 tok/s$1,39729.6
Mac mini M4 16GBQ8_0fits9 tok/s$1,3996.7
NVIDIA GeForce RTX 4070 SuperQ8_0fits38 tok/s$1,40026.9
AMD Radeon RX 7600 XTQ8_0fits19 tok/s$1,44013.2
NVIDIA GeForce RTX 5070 TiQ8_0fits67 tok/s$1,69039.5
NVIDIA GeForce RTX 4070 Ti SuperQ8_0fits50 tok/s$1,79028.0
NVIDIA GeForce RTX 4080 SuperQ8_0fits55 tok/s$2,00027.5
AMD Radeon RX 7900 XTQ8_0fits53 tok/s$2,08325.4
NVIDIA GeForce RTX 5080Q8_0fits72 tok/s$2,37030.2
NVIDIA GeForce RTX 3090Q8_0fits70 tok/s$2,80024.9
AMD Radeon AI PRO R9700Q8_0fits42 tok/s$2,80015.1
NVIDIA RTX PRO 6000 BlackwellQ8_0fits134 tok/s$3,13342.6
NVIDIA GeForce RTX 3090 TiQ8_0fits75 tok/s$3,99918.8
GMKtec EVO-X2 Ryzen AI Max+ 395 128GBQ8_0fits17 tok/s$5,0993.3
NVIDIA GeForce RTX 4090Q8_0fits75 tok/s$7,10710.6
ASUS Ascent GX10 128GBQ8_0fits20 tok/s$8,2992.5
NVIDIA GeForce RTX 5090Q8_0fits134 tok/s$10,30013.0
Mac Studio M4 Max 128GBQ8_0fits43 tok/s––
Mac mini M4 Pro 64GBQ8_0fits21 tok/s––
AMD Radeon RX 7900 XTXQ8_0fits64 tok/s––
Framework Desktop Ryzen AI Max+ 395 128GBQ8_0fits17 tok/s––
Mac Studio M3 Ultra 512GBQ8_0fits64 tok/s––
NVIDIA DGX Spark 128GBQ8_0fits20 tok/s––
Mac Studio M3 Ultra 256GBQ8_0fits64 tok/s––
Mac mini M4 Pro 48GBQ8_0fits21 tok/s––
NVIDIA GeForce RTX 4060 Ti 16GBQ8_0fits22 tok/s––
Mac Studio M4 Max 64GBQ8_0fits43 tok/s––
Mac Studio M3 Ultra 96GBQ8_0fits64 tok/s––
Framework Desktop Ryzen AI Max+ 395 64GBQ8_0fits17 tok/s––
Mac mini M4 32GBQ8_0fits9 tok/s––
NVIDIA Tesla P40Q8_0fits26 tok/s––

Memory needed

Weights, KV cache and runtime buffers at each context length (KV cache at fp16).

QuantFile@8k@32k@64k@128k
mlx-4bitmlx1.3 GB2.4 GB3.5 GB5.0 GB8.0 GB
Q4_K_Mgguf1.5 GB2.6 GB3.7 GB5.2 GB8.2 GB
Q5_K_Mgguf1.8 GB2.9 GB4.0 GB5.5 GB8.5 GB
Q6_Kgguf2.0 GB3.0 GB4.2 GB5.7 GB8.7 GB
mlx-8bitmlx2.5 GB3.6 GB4.7 GB6.2 GB9.2 GB
Q8_0gguf2.5 GB3.6 GB4.7 GB6.2 GB9.2 GB

Run it

Copy a command for the runtime you use.

  • mlx-4bit
    • LM Studiolms get mlx-community/MiniCPM5-2B-mlx-4Bit
    • MLXmlx_lm.server --model mlx-community/MiniCPM5-2B-mlx-4Bit
  • Q4_K_M
    • Ollamaollama run hf.co/bartowski/MiniCPM5-2B-GGUF:Q4_K_M
    • LM Studiolms get bartowski/MiniCPM5-2B-GGUF@q4_k_m
    • llama.cppllama-server -hf bartowski/MiniCPM5-2B-GGUF:Q4_K_M
  • Q5_K_M
    • Ollamaollama run hf.co/bartowski/MiniCPM5-2B-GGUF:Q5_K_M
    • LM Studiolms get bartowski/MiniCPM5-2B-GGUF@q5_k_m
    • llama.cppllama-server -hf bartowski/MiniCPM5-2B-GGUF:Q5_K_M
  • Q6_K
    • Ollamaollama run hf.co/bartowski/MiniCPM5-2B-GGUF:Q6_K
    • LM Studiolms get bartowski/MiniCPM5-2B-GGUF@q6_k
    • llama.cppllama-server -hf bartowski/MiniCPM5-2B-GGUF:Q6_K
  • mlx-8bit
    • LM Studiolms get mlx-community/MiniCPM5-2B-8bit
    • MLXmlx_lm.server --model mlx-community/MiniCPM5-2B-8bit
  • Q8_0
    • Ollamaollama run hf.co/bartowski/MiniCPM5-2B-GGUF:Q8_0
    • LM Studiolms get bartowski/MiniCPM5-2B-GGUF@q8_0
    • llama.cppllama-server -hf bartowski/MiniCPM5-2B-GGUF:Q8_0

Notes

Dense dual-language (en/zh) 2B model with standard LlamaForCausalLM architecture, aimed at on-device use. This is the final RL+OPD post-trained BF16 release; GGUF, MLX and GPTQ variants are separate repos.

Questions

How much VRAM does MiniCPM5 2B need?
At mlx-4bit, MiniCPM5 2B needs about 2 GB of memory at 8k context and 8 GB at 128k, counting the weights, the KV cache and runtime buffers.
What is the best GPU for running MiniCPM5 2B locally?
The cheapest hardware in Canada that runs MiniCPM5 2B fully in memory at 128k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 27 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 57 tok/s for $769). The fastest we price is the NVIDIA RTX PRO 6000 Blackwell, at about 134 tok/s.
Can I run MiniCPM5 2B on a 16 GB GPU?
Yes: at Q8_0 it fits fully in 16 GB at 128k context. On the NVIDIA GeForce RTX 5080 it runs at about 72 tok/s (estimated).
Can I run MiniCPM5 2B on a 24 GB GPU?
Yes: at Q8_0 it fits fully in 24 GB at 128k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 75 tok/s (estimated).
How do I run MiniCPM5 2B locally?
With Ollama: ollama run hf.co/bartowski/MiniCPM5-2B-GGUF:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.
What licence is MiniCPM5 2B released under?
Apache License 2.0, which allows commercial use.