Lasimeri/MiniMax-M2.7-int4-AutoRound

MiniMax-M2.7 INT4 AutoRound

4-bit quantized version of MiniMaxAI/MiniMax-M2.7 using Intel AutoRound.

Quantization Config

Setting	Value
Scheme	W4A16 (INT4 weights, FP16 activations)
Group size	128
Ignored layers	MoE gate layers (kept at full precision)
Method	RTN (iters=0)

Usage

vLLM

vllm serve Lasimeri/MiniMax-M2.7-int4-AutoRound \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-auto-tool-choice \
  --tool-call-parser minimax_m2 \
  --reasoning-parser minimax_m2_append_think

SGLang

python -m sglang.launch_server \
  --model-path Lasimeri/MiniMax-M2.7-int4-AutoRound \
  --trust-remote-code \
  --tp 8 \
  --reasoning-parser minimax-append-think \
  --tool-call-parser minimax-m2

Quantization Hardware

Quantized on a single-node rig:

Component	Spec
CPU	AMD EPYC 7742 (64C / 128T)
RAM	251 GB DDR4
GPUs	8× RTX 3080 (20 GB modded)

Peak resource usage during quantization: ~25.6 GB RAM, ~5 GB VRAM on GPU 0, ~1.3 GB on each remaining GPU.