notes/projects/neosphere/ml-server/20250711-qwerty_multi-GPU.md

Multi-GPU Setup: Boltz vs. Ollama+OpenWebUI

To maximize GPU utilization while running Boltz (likely a large-scale AI model) and Ollama+OpenWebUI (a smaller, lightweight LLM inference tool), here's a structured approach:

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1. Separate GPUs for Different Workloads

Best Practice:

• Use separate GPUs for Boltz and Ollama+OpenWebUI.
• Why:
  • Boltz likely requires high VRAM (e.g., 96 GB for H100) and low-latency inter-GPU communication (NVLink) for distributed tasks.
  • Ollama+OpenWebUI uses smaller models (e.g., 7B or less) and low VRAM (e.g., 16–32 GB).
  • Separating them avoids resource contention (e.g., VRAM, compute bandwidth) and ensures each tool gets optimal performance.

Implementation:

• Assign H100 to Boltz (via CUDA_VISIBLE_DEVICES=0).
• Assign L40 to Ollama+OpenWebUI (via CUDA_VISIBLE_DEVICES=1).
• Ensure both GPUs are recognized and functional via nvidia-smi.

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2. Shared GPU for Same Workload

Use Case:

• If both tools require the same GPU (e.g., for a single inference task or model parallelism), use NVLink for low-latency communication.
• Why:
  • NVLink (H100) or PCIe 4.0 (L40) enables cross-GPU data transfer for distributed inference or model parallelism.
  • Requires CUDA-aware MPI or Distributed Memory Frameworks (e.g., PyTorch Distributed, Horovod).

Implementation:

• Configure both tools to use both GPUs (e.g., CUDA_VISIBLE_DEVICES=0,1).
• Use NVLink (for H100) or PCIe 4.0 (for L40) to minimize latency.
• Ensure Boltz and Ollama are compatible with multi-GPU workflows (e.g., model sharding, pipeline parallelism).

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3. Key Considerations

Factor	Recommendation
GPU Allocation	Use separate GPUs for Boltz and Ollama+OpenWebUI to avoid resource contention.
Inter-GPU Communication	Use NVLink (H100) or PCIe 4.0 (L40) for shared GPU workflows.
Software Compatibility	Ensure Boltz and Ollama support multi-GPU setups (e.g., CUDA-aware MPI).
VRAM Utilization	Allocate H100 to Boltz (high VRAM) and L40 to Ollama (low VRAM).
Driver Configuration	Install NVIDIA drivers 535+ and ensure CUDA 12.x compatibility.

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4. Optimal Workflow

• Separate GPUs:

  • Boltz: Use H100 with NVLink for distributed training/inference.
  • Ollama+OpenWebUI: Use L40 for lightweight LLM inference.
  • Advantages: Maximized VRAM, reduced latency, and no resource contention.

• Shared GPU:

  • Boltz: Use H100 for large-scale tasks.
  • Ollama+OpenWebUI: Use H100 for lightweight tasks (e.g., model serving).
  • Advantages: Single GPU utilization, but requires careful resource management.

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5. Final Advice

• Prioritize Separate GPUs: For most use cases, Boltz and Ollama+OpenWebUI will benefit from dedicated GPUs.
• NVLink is Optional: Only use it if you need cross-GPU communication for advanced workflows (e.g., model parallelism).
• Monitor Performance: Use nvidia-smi and Prometheus/Grafana to track GPU utilization, VRAM, and latency.

By separating the workloads, you ensure maximum efficiency for both tools while leveraging the full potential of your hardware. 🚀