Model Details

Performance

Benchmarked on NVIDIA DGX Spark (Blackwell GB10, 128GB unified LPDDR5X):

PPL (Perplexity) measured on 8 diverse Korean texts (289 tokens total) using vLLM logprobs API. Lower is better.

Key finding: NVFP4 quantization achieves virtually identical quality to FP16 while being ~3x faster and using ~3x less memory.

Benchmark Results (Accuracy vs Original)

Evaluated using the same Chat CoT protocol as the original model (0-shot, chat template applied, exact_match on the generated answer — the Llama 3 evaluation methodology SKT used for A.X-3.1). This ensures a fair, apples-to-apples comparison between the original FP16 model and the NVFP4 quantized version.

Average recovery 97.8% across 5 benchmarks — NVFP4 4-bit quantization preserves nearly all of the original model's accuracy. On CLIcK the gap is just 0.10pp (essentially lossless).

Per-domain breakdown:

IFEval (4 sub-metrics): prompt-strict 81.89% · inst-strict 87.29% · prompt-loose 83.36% · inst-loose 88.61% (avg 85.29%)

Evaluation: lm-evaluation-harness via local-chat-completions, vLLM 0.19.1 on NVIDIA DGX Spark. Original FP16 scores from the skt/A.X-3.1 model card. Knowledge benchmarks use the 0-shot Chat CoT protocol (chat template + step-by-step reasoning + exact_match); MMLU uses flexible-extract on the full test split, and MATH uses math_verify (symbolic equivalence) — both to match the original's methodology. IFEval recovery is vs the 4-metric average.

How to Use

With vLLM (Recommended)

bash
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# Requires NVIDIA Blackwell GPU (sm_121a) and vLLM with NVFP4 support
vllm serve dlsxj101/A.X-3.1-NVFP4 \
  --quantization fp4 \
  --dtype float16 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.85

With vLLM Docker

bash
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docker run --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  ghcr.io/bjk110/vllm-spark:v019-ngc2603 \
  python3 -m vllm.entrypoints.openai.api_server \
  --model dlsxj101/A.X-3.1-NVFP4 \
  --quantization fp4 \
  --dtype float16 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.85 \
  --host 0.0.0.0 --port 8000

OpenAI-Compatible API

python
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from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

response = client.chat.completions.create(
    model="dlsxj101/A.X-3.1-NVFP4",
    messages=[{"role": "user", "content": "한국의 AI 산업 현황을 설명해주세요."}],
    max_tokens=1024,
    temperature=0.7,
)
print(response.choices[0].message.content)

Hardware Requirements

• GPU: NVIDIA Blackwell architecture (GB10, GB100, GB200, B100, B200)
  • NVFP4 is a Blackwell-native format computed directly on Tensor Cores
  • Not compatible with pre-Blackwell GPUs (A100, H100, etc.)
• Memory: ~21 GB GPU memory minimum
• Software: vLLM >= 0.19.0 with NVFP4 support

Quantization Details

• Algorithm: max (NVFP4_DEFAULT_CFG) — measures maximum activation values per tensor
• Group Size: 16
• Excluded Modules: lm_head (kept in FP16)
• Calibration: 8 English text samples (sufficient for max algorithm)
• Quantization Time: ~1 minute on DGX Spark

Qualitative Evaluation

Tested across 8 categories (Korean knowledge, logic, creative writing, coding, summarization, math, fact-checking, English):

• Korean Knowledge: Accurate, well-structured responses identical to FP16
• Logic/Reasoning: Correct problem-solving with proper mathematical notation
• Creative Writing: Natural Korean poetry with appropriate imagery
• Coding: Correct Python code with proper explanations
• Summarization: Concise and accurate 3-sentence summaries
• Math: Correct differentiation with step-by-step solutions
• Fact-Checking: Accurate historical information
• English: Clear, well-organized English explanations

License

This model is released under the Apache 2.0 license, same as the base model skt/A.X-3.1.

Acknowledgments

• Quantum Nexus — Quantization, benchmarking, and deployment performed on Quantum Nexus's NVIDIA DGX Spark (Blackwell GB10, 128GB)
• SKT for the original A.X-3.1 model
• NVIDIA for ModelOpt quantization toolkit and DGX Spark hardware
• vLLM team for NVFP4 inference support