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MiniMax M2.5

MoE

MiniMaxMiniMax · 228.7B (10B active) · Mixture of experts

MiniMax M2.5 is a 228.7B model from MiniMax with a 192K-token context. At Q4_K_M with an 8K context it needs about 131.1 GB; 165 GB leaves room for longer chats.

Hugging Face GGUF

Mixture of experts

Parameters: 228.7B · Active: 10B

All 228.7B parameters load into memory, but only 10B work on each token, so it runs at the speed of a much smaller model.

Quantization options

QuantMemoryOn your hardware
Q2_K—…
Q3_K_M—…
Q4_K_M—…
Q5_K_M—…
Q6_K—…
Q8_0—…
F16—…

Memory by context length

At Q4_K_M. The context cache grows with every token the model keeps in mind.

ContextContext cacheTotal
4K1 GB130.1 GB
8K1.9 GB131.1 GB
32K7.8 GB136.9 GB
128K31 GB160.1 GB

Share a measured speed

Run one of these and paste the whole output below (or just the tokens per second):

llama-bench -m model.gguf
ollama run model --verbose

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Next steps

Least hardware that runs it well

Desktop devices with the least memory that give MiniMax M2.5 grade A or S at Q4_K_M.

Can I run MiniMax M2.5 locally?

Can I run MiniMax M2.5 locally?

Yes, if your GPU or Mac has about 131.1 GB free for it at Q4_K_M. Open this page on that computer to see the grade for your exact hardware, then start it with llama.cpp, Ollama or LM Studio.

How much memory does MiniMax M2.5 need?

About 131.1 GB at Q4_K_M with an 8K context and 228.7 GB at Q8_0. Lower quantizations fit smaller cards with some loss in quality; longer contexts add to the total.