DeepSeek R1/V3 Local & Private Cluster Deployment: vLLM vs SGLang vs Ollama Benchmarks

The release of DeepSeek-V3 and its reasoning-specialized counterpart DeepSeek-R1 marked a watershed moment for enterprise open-weights AI: frontier reasoning capabilities delivered at open-weights accessibility. However, deploying a Mixture-of-Experts (MoE) architecture with hundreds of billions of total parameters in production requires addressing severe VRAM constraints and scheduling bottlenecks. In this report, the Radar Hardware Lab evaluates vLLM, SGLang, and Ollama across single-node workstations and multi-GPU servers.

01 Hardware Testbed & Benchmark Methodology

All evaluations were conducted in our physical laboratory across two standardized hardware tiers:

• Workstation Tier: 2× NVIDIA RTX 4090 (24GB VRAM each, 48GB total, PCIe 4.0 x16), Intel Core i9-14900K, 128GB DDR5 5600MHz RAM;
• Server Tier: 4× NVIDIA A100 (80GB SXM4, NVLink 600GB/s), Dual AMD EPYC 7763, 512GB ECC RAM.

02 Production Deployment Configuration with SGLang

For high-concurrency production endpoints requiring low Time-To-First-Token (TTFT) and high throughput, SGLang's RadixAttention tree-based KV cache reuse outperforms alternative runtimes by caching shared system prompts and multi-turn prefixes: