This document stores summaries of key discussions and recommendations related to the local LLM hardware project. Each entry contains the main points discussed and explains how they contribute to the overall project goal.
Hardware Components & Scaling Efficiency
Date: 2025-03-08
Summary:
Analysis of key hardware components for running LLMs locally and their scaling efficiency when adding multiple units:
- GPUs: Primary workhorse for inference. Good scaling for 2-4 GPUs (80-90% efficiency), diminishing returns beyond that.
- VRAM: Critical constraint, high scaling efficiency (~90-95%) with proper model parallelism.
- CPUs: Limited impact once sufficient cores for tokenization and orchestration are present.
- System RAM: Low scaling benefit beyond minimum requirements (64-128GB usually sufficient).
- Storage (SSDs): Important for model loading, limited scaling benefit for inference.
- Interconnects: Critical bottleneck in multi-GPU setups, NVLink superior to PCIe.
- Cooling & Power: Must scale with added components.
Recommendation Strategy:
- Start with one powerful GPU with maximum VRAM for budget
- Ensure good CPU and RAM foundation (12+ cores, 64GB+ RAM)
- Invest in fast storage (NVMe SSD with 2GB/s+ read speeds)
- Scale by adding similar GPUs if needed, but be aware of diminishing returns
Contribution to Project Goal:
This information establishes the foundational understanding of how different hardware components contribute to LLM performance and how efficiently they scale. This directly addresses the project goal by helping make informed decisions about whether to invest in fewer expensive components or multiple cheaper ones. Understanding scaling efficiency ensures the hardware solution will be both cost-effective and properly scaled for current and future needs.
Specific Hardware Recommendations
Date: 2025-03-08
Summary:
Based on our scaling efficiency analysis, here are specific hardware recommendations for a cost-effective, scalable local LLM setup:
Primary System (Starting Point)
Total Estimated Cost: $3,780-4,600 USD
- GPU: NVIDIA RTX 4090 (24GB VRAM) - Best consumer GPU for LLM inference with excellent VRAM capacity and compute performance
- CPU: AMD Ryzen 9 7950X (16 cores/32 threads) or Intel Core i9-13900K (24 cores) - Ample cores for tokenization and orchestration
- RAM: 64GB DDR5-6000 CL30 (2x32GB) - Fast memory with room for expansion
- Storage: 2TB Samsung 990 Pro NVMe SSD (7,450 MB/s read) - Ultra-fast storage for model loading
- Secondary Storage: 4-8TB SSD or HDD for model library storage
- Motherboard: High-quality PCIe 5.0 board with good multi-GPU support (e.g., ASUS ROG Strix X670E-E Gaming)
- Power Supply: 1200W 80+ Gold or better (room for adding a second GPU)
- Cooling: 360mm AIO for CPU, case with excellent airflow
Scaling Options (Future Upgrades)
- Additional GPUs: Add 1-3 more RTX 4090s for greater capacity ($1,600-1,800 each)
- Alternative: Specialized ML GPU: Consider NVIDIA RTX 6000 Ada (48GB) or A6000 (48GB) for double the VRAM in a single card ($6,500-7,500)
- RAM Expansion: Upgrade to 128GB if running multiple services alongside LLMs ($250-350 for additional 64GB)
Budget-Conscious Alternative
Total Estimated Cost: $2,110-2,770 USD
- GPU: NVIDIA RTX 4080 SUPER (16GB VRAM) - Good performance with moderate VRAM capacity
- CPU: AMD Ryzen 7 7800X3D or Intel Core i7-13700K - Still plenty for LLM inference needs
- RAM: 32GB DDR5-5600 (2x16GB) - Upgrade later if needed
- Storage: 1TB PCIe 4.0 NVMe SSD
- Other components: Scale down accordingly
Theoretical Maximum Scaling (Server-Grade)
Total Estimated Cost: $50,000+ USD
For future reference, maximum scaling would involve:
- Server motherboard with PCIe Gen 5 and NVLink support
- Multiple NVIDIA H100 (80GB) or A100 (80GB) GPUs ($10,000-35,000 each)
- EPYC or Xeon server CPUs ($2,000-7,000)
- 256GB+ ECC RAM ($1,500-3,000)
- Enterprise-grade cooling and redundant power
Contribution to Project Goal:
These specific hardware recommendations provide a concrete implementation of our scaling strategy, offering a cost-effective starting point that can be scaled as needed. The primary system balances performance and value, focusing resources where they matter most for LLM inference (GPU VRAM capacity and speed). The scaling options provide a clear upgrade path for future expansion, ensuring the solution will remain viable for the foreseeable future. The budget alternative and theoretical maximum configurations provide context for understanding the full spectrum of options.