
### **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. 🚀