I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs.
Note: MiniMax M2 architecture support in llama.cpp is still maturing. If you encounter inference issues, ensure you're using a recent llama.cpp build and report issues upstream.
Available Files
File
Profile
Size
Best For
MiniMax-M2.7-APEX-I-Balanced.gguf
I-Balanced
155 GB
Best overall quality/size ratio
MiniMax-M2.7-APEX-Balanced.gguf
Balanced
155 GB
General purpose
MiniMax-M2.7-APEX-I-Quality.gguf
I-Quality
129 GB
Highest quality with imatrix
MiniMax-M2.7-APEX-Quality.gguf
Quality
129 GB
Highest quality standard
MiniMax-M2.7-APEX-I-Compact.gguf
I-Compact
100 GB
Multi-GPU setups, best quality/size
MiniMax-M2.7-APEX-Compact.gguf
Compact
100 GB
Multi-GPU setups
MiniMax-M2.7-APEX-I-Mini.gguf
I-Mini
80 GB
Smallest "safe" tier
MiniMax-M2.7-APEX-I-Nano.gguf
I-Nano (new)
64 GB
Experimental — IQ2_XXS mid-layer experts
MiniMax-M2.7-APEX-F16-*.gguf
F16 reference
426 GB (10 shards)
Full-precision BF16 for imatrix/further research
What is APEX?
APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention and shared-expert tensors at higher precision.
See the APEX project for full details, technical report, and scripts.
Nano (new experimental tier)
APEX M2.7 debuts the Nano tier, which pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2_S, edges to Q3_K, and keeps shared experts at Q5_K. About 20% smaller than Mini with modest quality cost, viable only on MoE thanks to sparse per-token activation. Requires imatrix.
Benchmarks for Nano are pending. Feedback welcome.
