We get this question a lot. Running a local LLaMA model with 405B+ parameters and sharing it with 10 computers requires serious hardware, as these models consume massive amounts of VRAM, RAM, and compute power. Here’s how you would calculate the requirements and a recommended setup.
Step 1: Understanding Model Size and Hardware Needs
Memory (VRAM & RAM) Calculation
- LLaMA models store parameters in 16-bit (FP16) or 8-bit quantized format.
- 400B parameters in FP16:
400B * 2 bytes (16 bits) = 800 GB of VRAM
- 400B parameters in 8-bit quantization:
400B * 1 byte (8 bits) = 400 GB of VRAM
- You’ll need at least 400-800 GB VRAM or split across multiple GPUs.
CPU & RAM Requirements
- If offloading parts of the model to CPU, you’d need at least 2 TB of RAM.
- CPU should have high core count (e.g., AMD EPYC 9654 with 96 cores or Intel Xeon Platinum).
Storage (NVMe SSD)
- A high-speed NVMe SSD (e.g., 8 TB Gen4/Gen5) to store model weights for fast retrieval.
Networking for 10 Computers
- 10 GbE or higher networking, since each computer will need fast access to the model.
Step 2: Building the Best Hardware for Hosting
Option 1: Single High-End Server
- 8x NVIDIA H100 (80GB VRAM each, 640GB total)
- 2 TB DDR5 ECC RAM
- AMD EPYC 9654 (96-core) or Intel Xeon Platinum 8480+
- Dual 8 TB PCIe Gen5 NVMe SSDs
- 10/25/40 GbE Networking for Client Access
Option 2: Multi-Node Cluster (More Cost-Effective)
- 4x Workstations with 2x A100 (80GB) or H100 per node
- 512 GB – 1 TB RAM per node
- 10/40 GbE Networking Between Nodes
- Distributed inference using vLLM or FSDP (Fully Sharded Data Parallel) for efficiency
Step 3: Setting Up for 10 Clients
- Use vLLM, Tensor Parallelism (TP), or DeepSpeed to optimize inference across multiple GPUs.
- Deploy via API on LAN:
- Use vLLM with FastAPI/Triton Server to expose a REST API.
- gRPC-based servers for faster streaming responses.
- Use a dedicated inference server with load balancing (e.g., Redis + FastAPI).
Step 4: Power & Cooling
- Power Draw: Expect ~10-15 kW for a multi-GPU setup.
- Cooling: Requires liquid cooling or data center-grade AC.
Conclusion
Minimum viable setup:
- 4x A100 80GB GPUs (320GB VRAM total)
- 1 TB DDR5 RAM
- 4 TB PCIe Gen4 NVMe
- AMD EPYC 64-core
- 10 GbE Network
- NVMe SSD for fast access
Best Performance Setup:
- 8x H100 80GB (640GB VRAM)
- 2 TB RAM
- Dual AMD EPYC 9654
- Dual 8 TB PCIe Gen5 SSDs
- 40 GbE Networking
- Dedicated AI rack with water cooling
If you have unlimited budget, go with H100 cluster with Infiniband for maximum efficiency.
How Much it would cost:
Building a local LLaMA model with over 400 billion parameters and ensuring seamless sharing across 10 computers necessitates a substantial financial investment in high-performance hardware. Below is a breakdown of the estimated costs for the recommended setups:
Option 1: Single High-End Server
1. NVIDIA H100 GPUs:
- Quantity: 8
- Unit Price: Approximately $30,000 each
- Total: $240,000
2. System Memory (RAM):
- Capacity: 2 TB DDR5 ECC RAM
- Estimated Cost: $10,000 – $15,000
3. Processor:
- Model: AMD EPYC 9654 (96-core)
- Price: Approximately $11,805
4. Storage:
- Type: Dual 8 TB PCIe Gen5 NVMe SSDs
- Estimated Cost: $4,000 – $6,000
5. Networking:
- 10/25/40 GbE Networking Equipment: Approximately $5,000
6. Additional Components:
- Chassis, Power Supply, Cooling Systems, etc.: $10,000 – $15,000
Total Estimated Cost: $280,000 – $292,000
Option 2: Multi-Node Cluster
Each Node Configuration:
1. NVIDIA A100 GPUs:
- Quantity: 2 per node
- Unit Price: Approximately $20,000 each
- Total per Node: $40,000
2. System Memory (RAM):
- Capacity: 512 GB – 1 TB DDR5 ECC RAM
- Estimated Cost: $5,000 – $10,000
3. Processor:
- Model: AMD EPYC 9654 (96-core)
- Price: Approximately $11,805
4. Storage:
- Type: 4 TB PCIe Gen4 NVMe SSD
- Estimated Cost: $2,000 – $3,000
5. Networking:
- 10/40 GbE Networking Equipment: Approximately $3,000
6. Additional Components:
- Chassis, Power Supply, Cooling Systems, etc.: $8,000 – $12,000
Total Estimated Cost per Node: $69,805 – $79,805
Cluster Configuration:
- Number of Nodes: 4
- Total Cluster Cost: $279,220 – $319,220
Additional Considerations:
- Power and Cooling: High-performance hardware requires substantial power and advanced cooling solutions. Investing in efficient cooling systems and ensuring adequate power supply is crucial.
- Networking Infrastructure: To facilitate seamless sharing across 10 computers, investing in high-speed networking equipment is essential. This includes switches, routers, and cabling compatible with 10/25/40 GbE standards.
- Software and Licensing: Allocate a budget for necessary software licenses, including operating systems, machine learning frameworks, and any specialized tools required for model deployment and management.
- Maintenance and Support: Consider ongoing maintenance costs and potential support contracts to ensure the system remains operational and up-to-date.
Establishing a local LLaMA model with over 400 billion parameters and ensuring efficient sharing across 10 computers involves a significant financial investment, primarily driven by the cost of high-end GPUs and supporting hardware. Depending on the chosen configuration, the total estimated cost ranges from approximately $280,000 to $320,000. It’s advisable to consult with hardware vendors and system integrators to obtain precise quotations tailored to your specific requirements and to explore potential financing or leasing options to manage the investment effectively.
