MiniMax·MiniMax·MiniMaxM2ForCausalLM

MiniMax M2.7 — Hardware Requirements & GPU Compatibility

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MiniMax M2.7 is a 228.7B-parameter open language model from MiniMax in the MiniMax family. It supports a context window of up to 204,800 tokens. At Q4_K_M it needs about 137.78 GB of VRAM — see which GPUs and Macs can run it below.

983.8K downloads 1.2K likes 298.5K quant downloads205K context

Specifications

Publisher
MiniMax
Family
MiniMax
Parameters
228.7B
Architecture
MiniMaxM2ForCausalLM
Context Length
204,800 tokens
Vocabulary Size
200,064
Release Date
2026-04-09
License
Other

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How Much VRAM Does MiniMax M2.7 Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.4097.8 GB
Q3_K_S3.50100.6 GB
Q3_K_M3.90112.0 GB
Q4_04.00114.9 GB
Q4_K_M4.80137.8 GB
Q5_K_M5.70163.5 GB
Q6_K6.60189.2 GB
Q8_08.00229.3 GB

Which GPUs Can Run MiniMax M2.7?

Q4_K_M · 137.8 GB

MiniMax M2.7 (Q4_K_M) requires 137.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 180+ GB is recommended. Using the full 205K context window can add up to 25.8 GB, bringing total usage to 163.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run MiniMax M2.7?

Q4_K_M · 137.8 GB

6 devices with unified memory can run MiniMax M2.7, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).

Where to Download MiniMax M2.7

Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.

Related Models

Frequently Asked Questions

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 requires 137.8 GB of VRAM at Q4_K_M, or 458.0 GB at BF16. Full 205K context adds up to 25.8 GB (163.5 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 228.7B × 4.8 bits ÷ 8 = 137.2 GB

KV Cache + Overhead 0.6 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 26.3 GB (at full 205K context)

VRAM usage by quantization

137.8 GB
163.5 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run MiniMax M2.7?

No — MiniMax M2.7 requires at least 63.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for MiniMax M2.7?

For MiniMax M2.7, Q4_K_M (137.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (140.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 63.5 GB.

VRAM requirement by quantization

IQ2_XXS
63.5 GB
IQ3_S
97.8 GB
Q3_K_L
117.8 GB
Q4_K_M
137.8 GB
Q4_K_L
140.6 GB
BF16
458.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run MiniMax M2.7 on a Mac?

MiniMax M2.7 requires at least 63.5 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run MiniMax M2.7 locally?

Yes — MiniMax M2.7 can run locally on consumer hardware. At Q4_K_M quantization it needs 137.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is MiniMax M2.7?

At Q4_K_M, MiniMax M2.7 can reach ~32 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B2008000 ÷ 137.8 × 0.65 = ~38 tok/s

Estimated speed at Q4_K_M (137.8 GB)

~38 tok/s
~38 tok/s
~32 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of MiniMax M2.7?

At Q4_K_M, the download is about 137.22 GB. The full-precision BF16 version is 457.41 GB. The smallest option (IQ2_XXS) is 62.89 GB.

Which GPUs can run MiniMax M2.7?

No single consumer GPU has enough VRAM to run MiniMax M2.7 at Q4_K_M (137.8 GB). Multi-GPU or professional hardware is required.

Which devices can run MiniMax M2.7?

6 devices with unified memory can run MiniMax M2.7 at Q4_K_M (137.8 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.