daudaudinang/Quyet-1.0-Large-Mixed-NVFP4
AI model by daudaudinang
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High-Throughput, Calibrated Decision Model with W4A16 NVFP4 Quantization for NVIDIA Blackwell Compressed to 24.3 GB (from 62.5 GB BF16) • 20.5 GB Serving VRAM • 97.84% Parity • 3.2x Higher Throughput Quyet-1.0-Large-Mixed-NVFP4 is an optimized, mixed-precision NVFP4 (W4A16NVFP4) quantized checkpoint of chinhnc/Quyet-1.0-Large (31.3B parameters, Gemma-4-31B-it architecture). Calibrated with NVIDIA ModelOpt specifically for the NVIDIA Blackwell architecture (GB10 / GB200 / B200, sm121 / sm120), this model compresses the 62.5 GB BF16 weights down to 24.3 GB while slashing active serving VRAM from 71.8 GB to 20.5 GB (-71.4% memory footprint). In extensive empirical benchmarks across both the JevBench standard suite (231 cases) and the EdgeBench Bilingual Enterprise suite (1,000 cases), the Mixed NVFP4 model achieves 97.84% exact decision parity with original BF16 weights, and delivers 3.2x higher serving throughput on an NVIDIA GB10 system. Associated Benchmark Dataset: daudaudinang/edgebench-bilingual-jev. Quantizing a 31B decision model to 4-bit requires careful precision balancing to…
Read the full model card ↗ · Preview checked 2026-10-30T13:41:28.250Z
Inside the original model card — Document outline
- Quyet-1.0-Large-Mixed-NVFP4
- 📌 Executive Summary
- 🎯 Model Specifications
- 🔬 Mixed-Precision Quantization Architecture
- 📊 Comprehensive Benchmark Evaluations
- 1. Standard JevBench Benchmark (231 Cases)
- 2. EdgeBench Bilingual Enterprise Benchmark (1,000 Cases)
- Breakdown by Enterprise Task Category (200 cases each):
- Breakdown by Language:
- 3. Context Scaling Profile (Latency vs Length on GB10)
- 🚀 How to Deploy & Serve
- 1. Run with vLLM (Docker)
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Model overview
daudaudinang/Quyet-1.0-Large-Mixed-NVFP4 is a task-unspecified model repository published by daudaudinang on Hugging Face. A library was not reported.
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- Created
- 2026-10-10T06:40:50.000Z
- Last modified
- 2026-10-10T06:40:50.000Z
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