65 lines
3.6 KiB
Markdown
65 lines
3.6 KiB
Markdown
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### **Multi-GPU Setup: Boltz vs. Ollama+OpenWebUI**
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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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---
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### **1. Separate GPUs for Different Workloads**
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**Best Practice**:
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- **Use separate GPUs** for **Boltz** and **Ollama+OpenWebUI**.
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- **Why**:
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- **Boltz** likely requires **high VRAM** (e.g., 96 GB for H100) and **low-latency inter-GPU communication** (NVLink) for distributed tasks.
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- **Ollama+OpenWebUI** uses **smaller models** (e.g., 7B or less) and **low VRAM** (e.g., 16–32 GB).
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- Separating them avoids **resource contention** (e.g., VRAM, compute bandwidth) and ensures each tool gets optimal performance.
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**Implementation**:
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- Assign **H100** to **Boltz** (via `CUDA_VISIBLE_DEVICES=0`).
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- Assign **L40** to **Ollama+OpenWebUI** (via `CUDA_VISIBLE_DEVICES=1`).
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- Ensure both GPUs are **recognized and functional** via `nvidia-smi`.
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---
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### **2. Shared GPU for Same Workload**
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**Use Case**:
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- If both tools **require the same GPU** (e.g., for a single inference task or model parallelism), use **NVLink** for **low-latency communication**.
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- **Why**:
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- NVLink (H100) or PCIe 4.0 (L40) enables **cross-GPU data transfer** for distributed inference or model parallelism.
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- Requires **CUDA-aware MPI** or **Distributed Memory Frameworks** (e.g., PyTorch Distributed, Horovod).
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**Implementation**:
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- Configure both tools to use **both GPUs** (e.g., `CUDA_VISIBLE_DEVICES=0,1`).
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- Use **NVLink** (for H100) or **PCIe 4.0** (for L40) to minimize latency.
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- Ensure **Boltz** and **Ollama** are compatible with **multi-GPU workflows** (e.g., model sharding, pipeline parallelism).
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---
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### **3. Key Considerations**
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| **Factor** | **Recommendation** |
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|--------------------------|-------------------------------------------------------------------------------------|
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| **GPU Allocation** | Use **separate GPUs** for Boltz and Ollama+OpenWebUI to avoid resource contention. |
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| **Inter-GPU Communication** | Use **NVLink** (H100) or **PCIe 4.0** (L40) for shared GPU workflows. |
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| **Software Compatibility** | Ensure **Boltz** and **Ollama** support multi-GPU setups (e.g., CUDA-aware MPI). |
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| **VRAM Utilization** | Allocate **H100** to Boltz (high VRAM) and **L40** to Ollama (low VRAM). |
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| **Driver Configuration** | Install **NVIDIA drivers 535+** and ensure **CUDA 12.x** compatibility. |
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---
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### **4. Optimal Workflow**
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- **Separate GPUs**:
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- **Boltz**: Use H100 with **NVLink** for distributed training/inference.
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- **Ollama+OpenWebUI**: Use L40 for lightweight LLM inference.
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- **Advantages**: Maximized VRAM, reduced latency, and no resource contention.
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- **Shared GPU**:
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- **Boltz**: Use H100 for large-scale tasks.
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- **Ollama+OpenWebUI**: Use H100 for lightweight tasks (e.g., model serving).
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- **Advantages**: Single GPU utilization, but requires careful resource management.
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---
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### **5. Final Advice**
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- **Prioritize Separate GPUs**: For most use cases, **Boltz** and **Ollama+OpenWebUI** will benefit from **dedicated GPUs**.
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- **NVLink is Optional**: Only use it if you need **cross-GPU communication** for advanced workflows (e.g., model parallelism).
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- **Monitor Performance**: Use `nvidia-smi` and **Prometheus/Grafana** to track GPU utilization, VRAM, and latency.
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By separating the workloads, you ensure **maximum efficiency** for both tools while leveraging the full potential of your hardware. 🚀 |