📖 Open LLM Definitions & Best Use Cases
Architectural breakdown, target workflows, and recommended deployment scenarios for all 35+ supported open-source models.
🖥️ My System Configurator & LLM Farm Blueprint
Calculate instance capacity, concurrent user scaling, and learn how to build high-efficiency multi-GPU LLM farms.
🧮 "How Many of Model X Can My System Run?"
System Capacity & Concurrent User Metrics
🚜 How Do I Start an LLM Farm?
An LLM Farm is a cluster of high-density GPU compute nodes (or consumer multi-GPU rigs) orchestrated to serve continuous inference APIs.
- Hardware Selection: Mix consumer high-VRAM cards (RTX 3090/4090 24GB) or enterprise PCIe accelerators (A100/H100).
- Unified API Gateway: Run an open proxy like
LiteLLMorOpen-WebUIin front of your nodes to load balance incoming prompts. - Inference Backend: Deploy
vLLMorTGIfor production API endpoints, orOllamafor local containerized instances. - Storage Subsystem: Use high-speed NVMe storage or 10GbE local NAS for instant model weights loading across nodes.
⚡ How to Optimize Resources for Maximum Performance
- Enable Continuous Batching (vLLM): Traditional sequential inference wastes GPU cycles. Continuous batching packs dynamic user requests together for 10x-20x throughput gains.
- FlashAttention-2 & PagedAttention: Drastically reduces KV cache memory consumption, allowing up to 5x larger batch sizes per GPU.
- Tensor Parallelism vs. Pipeline Parallelism: For models exceeding a single GPU's VRAM (e.g. 70B models), split layers across GPUs via
--tensor-parallel-size Non high-bandwidth PCIe/NVLink lanes. - Use AWQ / GGUF Quantization: AWQ (Activation-aware Weight Quantization) retains 99% of FP16 accuracy while slashing memory footprint by 60-70%.
👥 How Many Users Will My Execution Environment Support?
User capacity depends directly on whether you are serving serial chat completions or using a batched API server engine:
Batched Engine (vLLM with PagedAttention on RTX 4090): ~25 to 50 active users
Dual A100 Cluster (vLLM Tensor Parallel=2): ~200 to 500 active users (at 40+ tok/s/user)
🔧 Optimized Max LLM Execution Environment Checklist
Linux (Ubuntu/RHEL 9), Kernel 6.x+, NVIDIA Driver 550+, CUDA 12.4+, Hugepages enabled.
PCIe Gen 4.0 or Gen 5.0 x16 slots. Avoid x4/x1 riser slots for multi-GPU Tensor Parallelism.
Ensure 80+ Gold/Platinum PSU with 1.2kW+ capacity for dual/quad 3090/4090 setups. Maintain ambient <75°C GPU temps.
🎮 Will My GPU Run This Model?
Select your graphics card (or custom VRAM) to instantly test local runnability across all open-source models.
Runnability Report for 12 GB VRAM
Interactive VRAM Requirement Calculator
Select model parameters, quantization level, and target context length to calculate required GPU memory.
VRAM Calculation Breakdown
Open LLM Benchmark Comparison Matrix
| Model Name | Creator | Params | Ollama Tag | MMLU Score | HumanEval (Code) | Max Context | VRAM (Q4) |
|---|