MiniMax M2.5 2.5B
By MiniMaxAI · China
Updated 2026-08-28
Overview
A compact 2.5B model from MiniMaxAI under Apache 2.0, pairing a 128K context window with multilingual code and reasoning ability in a footprint small enough for edge hardware.
When to pick this model
- Edge or CPU-bound deployments needing long context
- Lightweight multilingual code assistance
- Apache 2.0 projects requiring unrestricted commercial use
- Prototyping before committing to a larger 7B-14B model
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 1.4 GB |
| Q5_K_M | 1.8 GB |
| Q8_0 | 2.7 GB |
| FP16 (no quantization) | 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, MiniMax M2.5 2.5B fits an 8 GB consumer card at Q4_K_M (1.4 GB). Stepping up to Q8_0 nearly doubles the footprint to 2.7 GB, and unquantized FP16 weights take 5 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, MiniMax M2.5 2.5B needs roughly 3.2 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 85 tokens/sec on a mid-range card, and up to 130 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches MiniMax M2.5 2.5B 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 MiniMax M2.5 2.5B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (5 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (5 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (5 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (5 GB used) |
| 32 GB | RTX 5090 | FP16 (5 GB used) |
Which GPU should you buy to run MiniMax M2.5 2.5B?
To run MiniMax M2.5 2.5B locally at Q4, you need ~1.4 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Fully permissive Apache 2.0 license
- 128K context window at just 2.5B parameters
- Runs in 1.4GB VRAM at Q4
- Multilingual code and reasoning ability
Limitations
- Capabilities inherently limited at 2.5B parameters
- Gated model on Hugging Face
- Less versatile than 7B-14B class models
Typical workloads
In our catalog grid, MiniMax M2.5 2.5B is filed under Edge/CPU, Compact Code, Multilingual Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 125k-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 · 2.5B parameters · 128K context
Training: MiniMax M2.5 family (MiniMaxAI, China). Compact Apache 2.0 variant focused on code and multilingual reasoning.
A small, Apache-2.0 multilingual coder/reasoner with surprisingly long context for its size — ideal for constrained hardware.
Quick start
ollama pull minimax-m2.5Or 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 MiniMax M2.5 2.5B need?
At the recommended Q4_K_M quantization, MiniMax M2.5 2.5B needs about 1.4 GB of VRAM. Q8_0 takes 2.7 GB, and unquantized FP16 weights take 5 GB.
Can MiniMax M2.5 2.5B run without a GPU?
Yes — with roughly 3.2 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 MiniMax M2.5 2.5B support?
MiniMax M2.5 2.5B supports a 125k-token context window (128,000 tokens).
Can I use MiniMax M2.5 2.5B commercially?
Yes. MiniMax M2.5 2.5B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is MiniMax M2.5 2.5B on consumer hardware?
Our compatibility engine estimates on the order of 85 tokens/sec on a mid-range GPU and up to 130 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of MiniMax M2.5 2.5B should I download first?
Start with Q4_K_M (1.4 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.