⚡ Each donation = another big MoE quantized
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.
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💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.
Mistral-Small-4-119B APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of Mistral-Small-4-119B-2603.
Brought to you by the LocalAI team | APEX Project | Technical Report
Benchmark Results
Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| Mistral-Small-4-119B-APEX-I-Balanced.gguf | I-Balanced | ~72 GB | Best overall quality/size ratio |
| Mistral-Small-4-119B-APEX-I-Quality.gguf | I-Quality | ~62 GB | Highest quality with imatrix |
| Mistral-Small-4-119B-APEX-Quality.gguf | Quality | ~62 GB | Highest quality standard |
| Mistral-Small-4-119B-APEX-Balanced.gguf | Balanced | ~72 GB | General purpose |
| Mistral-Small-4-119B-APEX-I-Compact.gguf | I-Compact | ~48 GB | Multi-GPU setups, best quality/size |
| Mistral-Small-4-119B-APEX-Compact.gguf | Compact | ~48 GB | Multi-GPU setups |
| Mistral-Small-4-119B-APEX-I-Mini.gguf | I-Mini | ~38 GB | Smallest viable |
| mmproj.gguf | Vision projector | ~827 MB | Required for image understanding |
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).
See the APEX project for full details, technical report, and scripts.
Architecture
- Model: Mistral-Small-4-119B-2603 (Mistral4/DeepSeek-V2 style)
- Layers: 36
- Experts: 128 routed + 1 shared (4 active per token)
- Total Parameters: ~119B
- Active Parameters: ~11-12B per token
- Attention: Multi-head Latent Attention (MLA, kv_lora_rank=256, q_lora_rank=1024)
- Vision: Pixtral encoder (mmproj included)
- Context: 1M tokens (YaRN RoPE)
- APEX Config: 5+5 symmetric edge gradient across 36 layers, MLA-aware tensor mapping
Run with LocalAI
local-ai run mudler/Mistral-Small-4-119B-2603-APEX-GGUF@Mistral-Small-4-119B-APEX-I-Balanced.gguf
Credits
APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.