gpt-oss-120b-1024-Calibration-FP8

gpt-oss-120b-1024-Calibration-FP8

Premium FP8 quantization with 1,024-sample calibration across 4 diverse datasets

This is a premium FP8 quantized version of openai/gpt-oss-120b featuring rigorous multi-dataset calibration for production-grade reliability. Quantized by TevunahAi on enterprise-grade hardware.

🎯 Recommended Usage: vLLM

For optimal performance with full FP8 benefits and efficient MoE routing, use vLLM or TensorRT-LLM:

Quick Start with vLLM

pip install vllm

Python API:

from vllm import LLM, SamplingParams

# vLLM auto-detects FP8 from model config
llm = LLM(model="TevunahAi/gpt-oss-120b-1024-Calibration-FP8", dtype="auto")

# Generate
messages = [{"role": "user", "content": "Explain quantum computing"}]
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/gpt-oss-120b-1024-Calibration-FP8")
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

sampling_params = SamplingParams(temperature=0.7, max_tokens=512)
outputs = llm.generate([prompt], sampling_params)

for output in outputs:
    print(output.outputs[0].text)

OpenAI-Compatible API Server:

vllm serve TevunahAi/gpt-oss-120b-1024-Calibration-FP8 \
    --dtype auto \
    --max-model-len 8192

Then use with OpenAI client:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-abc123",  # dummy key
)

response = client.chat.completions.create(
    model="TevunahAi/gpt-oss-120b-1024-Calibration-FP8",
    messages=[
        {"role": "user", "content": "Explain quantum computing"}
    ],
    temperature=0.7,
    max_tokens=512,
)

print(response.choices[0].message.content)

vLLM Benefits

• ✅ Weights, activations, and KV cache in FP8
• ✅ ~60GB VRAM (for 120B MoE model!)
• ✅ Native FP8 tensor core acceleration on Ada/Hopper GPUs
• ✅ Efficient MoE routing - only 5B active per token
• ✅ 120B model capability at 5B model speed
• ✅ Premium 1024-sample calibration for production reliability

⚠️ Transformers: Not Practical

This model can be loaded with transformers, but will decompress FP8 → BF16 during inference, requiring significant VRAM. For large MoE models, vLLM is strongly recommended.

<details> <summary>Transformers Example (Not Recommended - Click to expand)</summary>

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Loads FP8 weights but decompresses to BF16 during compute
model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/gpt-oss-120b-1024-Calibration-FP8",
    device_map="auto",
    torch_dtype="auto",
    low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/gpt-oss-120b-1024-Calibration-FP8")

# Generate
messages = [{"role": "user", "content": "Explain quantum computing"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Requirements:

pip install torch>=2.1.0 transformers>=4.40.0 accelerate compressed-tensors

System Requirements:

• ~120GB+ VRAM (decompressed to BF16)
• Multi-GPU setup or H100 NVL
• Not practical for most deployments

⚠️ Critical: vLLM is the recommended deployment method for large MoE models.

</details>

📊 Model Details

🏆 Premium Calibration

This model was quantized using TevunahAi's premium multi-dataset calibration process:

Calibration Details

• Total Samples: 1,024 (4x industry standard)
• Datasets Used: 4 complementary sources
• Coverage: Comprehensive across all use cases

Why Premium Calibration?

Most FP8 quantizations use 128-512 samples from a single dataset. TevunahAi uses 1,024 samples across 4 diverse datasets, ensuring:

• ✅ Superior robustness across task types
• ✅ Better statistical coverage for quantization scales
• ✅ Minimal quality loss compared to FP16
• ✅ Production-grade reliability
• ✅ Consistent performance on edge cases

When quality matters, choose TevunahAi premium calibration quantizations.

🚀 MoE Architecture

GPT-OSS-120B uses an advanced Mixture of Experts (MoE) architecture:

How it works:

1. 120B total parameters split across expert networks
2. Router network selects which experts to activate
3. 5B active parameters per token (sparse activation)
4. Result: 120B model knowledge with 5B model speed

Benefits:

• ✅ Massive parameter count without massive compute
• ✅ Specialist experts for different types of knowledge
• ✅ Better quality-per-parameter ratio than dense models
• ✅ More accessible than equivalent dense models

With FP8 + MoE:

• ~60GB VRAM (vs ~240GB for FP16 dense equivalent)
• Inference speed comparable to 5B dense models
• Performance approaching 120B dense models

🔧 Why FP8 for Large MoE Models?

With vLLM/TensorRT-LLM:

• ✅ 50% memory reduction vs BF16 (~120GB → ~60GB)
• ✅ Dual RTX 4090 deployment or single A100 80GB / H100 80GB
• ✅ Faster inference via native FP8 tensor cores
• ✅ Efficient MoE routing - optimal for sparse activation
• ✅ 120B capability at 5B speed - best of both worlds

The MoE Advantage:

• Total Parameters: 120B (full model capability)
• Active Parameters: 5B per token (fast inference)
• Memory: ~60GB with FP8 (accessible on high-end prosumer hardware)
• Speed: Similar to dense 5B models
• Quality: Comparable to dense 120B models

FP8 + Premium Calibration + MoE = flagship model performance on workstation hardware.

💾 Model Files

This model is sharded into multiple safetensors files (all required for inference). The compressed format enables efficient storage and faster downloads.

🔬 Quantization Infrastructure

Professional hardware pushing the limits:

• CPUs: Dual Intel Xeon Max 9480 (224 threads, 128GB HBM2e @ 2000 GB/s)
• Memory: 256GB DDR5-4800 (16 DIMMs, 8-channel per socket, ~614 GB/s)
• Total Memory Bandwidth: ~2,614 GB/s aggregate
• Peak Memory Usage: ~310GB during quantization
• GPU: NVIDIA RTX 5000 Ada Generation (32GB VRAM, native FP8 support)
• Software: Ubuntu 25.10 | Python 3.12 | PyTorch 2.8 | CUDA 13.0 | llm-compressor

Why This Matters:

• This 120B MoE quantization required ~310GB of RAM during calibration
• The 1,024-sample multi-dataset calibration process is impossible on consumer hardware
• Professional infrastructure enables production-grade quantization quality

📚 About GPT-OSS

GPT-OSS-120B is OpenAI's flagship open-source model release, featuring:

• State-of-the-art performance across benchmarks
• Efficient MoE architecture (120B total, 5B active)
• Strong reasoning and instruction following
• Apache 2.0 license

🔧 Hardware Requirements

Minimum (vLLM):

• GPU: 2x RTX 4090 (48GB total) or A100 80GB
• VRAM: 60GB minimum
• CUDA: 11.8 or newer

Recommended (vLLM):

• GPU: H100 80GB / H100 NVL / 2x RTX 4090
• VRAM: 60GB+
• CUDA: 12.0+

Transformers:

• GPU: Multi-GPU setup or H100 NVL
• VRAM: 120GB+ total
• Not recommended - use vLLM instead

📖 Additional Resources

• vLLM Documentation: docs.vllm.ai
• TensorRT-LLM: github.com/NVIDIA/TensorRT-LLM
• TevunahAi Models: huggingface.co/TevunahAi
• llm-compressor: github.com/vllm-project/llm-compressor

📄 License

This model inherits the Apache 2.0 License from the original GPT-OSS model.

🙏 Acknowledgments

• Original Model: OpenAI
• Quantization Framework: Neural Magic's llm-compressor
• Quantized by: TevunahAi

📝 Citation

If you use GPT-OSS, please cite the original work:

@misc{gptoss2024,
  title={GPT-OSS: OpenAI's Open-Source Model Release},
  author={OpenAI},
  year={2024},
  url={https://huggingface.co/openai/gpt-oss-120b}
}

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🌟 Why TevunahAi Premium Calibration FP8?

The Difference is in the Details

Professional Infrastructure

• 2.6 TB/s aggregate memory bandwidth
• 310GB peak usage during 120B quantization
• 1,024 samples across 4 complementary datasets
• Quality-first approach over speed
• Enterprise-ready results

Pushing the Limits

This 120B MoE model required ~310GB of RAM during quantization — pushing our professional hardware to its limits. This level of rigorous calibration would be impossible on consumer hardware.

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