CormacGarvey examines the deployment and benchmarking of the DeepSeek R1 reasoning model on Azure ND_H100_v5 nodes using vLLM, providing practical insights into infrastructure demands and performance.

Performance Analysis: DeepSeek R1 Inference with vLLM on Azure ND-H100-v5

Introduction

The DeepSeek R1 model heralds a new era for large-scale reasoning in AI, but its deployment is far from trivial. This article guides practitioners through benchmark results, infrastructure demands, and cost-performance trade-offs for running DeepSeek R1 on Azure ND_H100_v5 hardware.

Benchmark Environment

  • Hardware: 2 Azure ND_H100_v5 nodes, each with 8 NVIDIA H100 GPUs (total 16 GPUs)
  • Interconnect: InfiniBand and NVLink for optimal memory bandwidth and low latency
  • Inference Server: vLLM used for scalable API-based model serving
  • Benchmarking Tool: vLLM bench with the AI-MO/aimo-validation-aime dataset from Hugging Face

Key Results

Reasoning Model Output

  • DeepSeek R1 produces extensive chain-of-thought reasoning, demonstrated by the generation of 1162 completion tokens on a simple numeric comparison prompt, compared to only 37 tokens by Llama 3.1 8B.
  • For simple prompts, DeepSeek R1’s detailed reasoning is often excessive and incurs higher costs and latency.

Throughput, Latency, and Cost

  • Throughput: DeepSeek R1 generates tokens much more slowly—about 54 times slower than Llama 3.1 8B on comparable GPU hardware.
  • Latency: Both TTFT (Time-To-First-Token) and ITL (Inter-Token Latency) are significantly higher (~6x and ~3x, respectively, over smaller models).
  • Cost: Token generation cost for DeepSeek R1 is approximately 54x that of Llama 3.1 8B (on 16 H100s), and about 34x higher on 8 H200s.
  • Resource Utilization: Actual network usage is modest (~14% of InfiniBand bandwidth; <1% of NVLink).

Infrastructure Setup

To deploy DeepSeek R1 for inference:

  • Install vLLM and FlashInfer, with required CUDA and Ninja dependencies.
  • Configure environment variables for each node, ensuring vLLM’s expert/data parallelism and backend flags are set for low-latency or throughput-optimized runs.
  • Scripts are provided for node configuration as well as for benchmarking via vLLM bench.

Analysis and Recommendations

  • DeepSeek R1 is well-suited for applications requiring deep, multi-step logical reasoning where model output quality justifies infrastructure cost.
  • For standard inference or cost-constrained scenarios, smaller models such as Llama 3.1 8B are greatly more efficient and economical.
  • GPU memory bandwidth and compute FLOPS are the primary bottlenecks, not network interconnect.
  • Practitioners should reserve R1 deployments for cases where its advanced reasoning is a must-have feature.

Example: API Usage

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-R1",
    "messages": [{"role": "user", "content": "9.11 and 9.8, which is greater? Explain your reasoning"}]
}'

References

Appendix: Installation and Configuration

Step-by-step scripts and command sequences for installing dependencies, preparing the environment, and configuring both nodes are included in the article body above.

Conclusion

DeepSeek R1 is a powerful reasoning LLM, but its resource and cost requirements limit its practicality to use cases demanding the highest reasoning quality. Azure’s ND_H100_v5 series provides the necessary infrastructure, but careful cost-benefit analysis is advised. For most projects, lighter-weight models will offer superior efficiency.

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