Deepseek Blackwell Optimization
Next-generation AI architecture powering DeepSeek-R1-FP4 model with unprecedented performance and efficiency for large language models.
API Usage Example
Deploy DeepSeek-R1-FP4 with TensorRT-LLM using this simple Python code:
from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
def main():
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(max_tokens=32)
llm = LLM(model="nvidia/DeepSeek-R1-FP4", tensor_parallel_size=8, enable_attention_dp=True)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
# The entry point of the program need to be protected for spawning processes.
if __name__ == '__main__':
main()Note: This example requires 8xB200 GPUs with TensorRT-LLM built from the latest main branch.
Key Features
- DeepSeek-R1-FP4 Model
Quantized version of DeepSeek AI's R1 model optimized for NVIDIA Blackwell architecture, reducing parameter bits from 8 to 4 while maintaining performance.
- TensorRT-LLM Optimization
Leverages NVIDIA's TensorRT-LLM for high-performance inference, enabling efficient deployment on Blackwell GPUs with reduced memory requirements.
- 128K Context Length
Supports extended context length up to 128K tokens, enabling comprehensive analysis of long documents and conversations with maintained coherence.
- 1.6x Memory Reduction
FP4 quantization reduces disk size and GPU memory requirements by approximately 1.6x compared to 8-bit models, enabling more efficient deployment.
Frequently Asked Questions
- What is NVIDIA Blackwell?
- NVIDIA Blackwell is a next-generation AI architecture designed to deliver unprecedented performance and efficiency for large language models and other AI workloads. It's the hardware platform that powers the DeepSeek-R1-FP4 model.
- What is DeepSeek-R1-FP4?
- DeepSeek-R1-FP4 is the quantized version of DeepSeek AI's R1 model, optimized for NVIDIA Blackwell architecture. It uses FP4 quantization to reduce memory requirements while maintaining high performance for inference tasks.
- Why use FP4 quantization?
- FP4 quantization reduces the number of bits per parameter from 8 to 4, resulting in approximately 1.6x reduction in disk size and GPU memory requirements. This enables more efficient deployment of large language models without significant performance degradation.
- How can I deploy the DeepSeek-R1-FP4 model?
- The model can be deployed using TensorRT-LLM on NVIDIA Blackwell GPUs. Sample code for deployment is available on the Hugging Face model page, and you'll need 8xB200 GPUs with TensorRT-LLM built from the latest main branch.