vLLM hardware, explained.

vLLM is an open-source high-throughput, memory-efficient inference and serving engine for large language models. Originally developed at UC Berkeley's Sky Computing Lab, vLLM introduced PagedAttention for efficient KV cache memory management and continuous batching for high request throughput, delivering up to 24x higher throughput than vanilla Hugging Face Transformers for multi-user LLM serving. vLLM provides an OpenAI-compatible API server, supports tensor parallelism for multi-GPU inference, integrates speculative decoding for latency reduction, and supports quantized models (AWQ, GPTQ, FP8). It is the production gold standard for serving Llama, Mistral, Qwen, DeepSeek, and most other open-source LLMs at scale.

vLLM requires NVIDIA GPUs with CUDA support. Practical hardware: a CUDA-capable NVIDIA GPU with VRAM sized to your model (24-32GB for 7B-13B models, 80-96GB for 70B models with quantization, multi-GPU for 70B+ in full precision), a CPU with adequate PCIe Gen5 lanes (AMD Ryzen 9 or Threadripper PRO for single GPU, Threadripper PRO or EPYC for multi-GPU), system RAM at 1.5x to 2x VRAM, fast NVMe storage for model weights (a 70B model is 140GB at FP16), and Linux as the operating system (vLLM is Linux-only, no Windows native support). For multi-GPU tensor parallelism, NVLink interconnect is preferred but not strictly required. Browse vLLM-ready AI workstations at vrlatech.com/vrla-tech-workstations/ai-deep-learning-workstations-high-performance-computing.

PagedAttention is vLLM's signature memory management technique, inspired by operating system virtual memory paging. Instead of allocating contiguous KV cache memory for each request (which leads to severe fragmentation and waste), PagedAttention divides KV cache into fixed-size blocks and manages them like virtual memory pages. The result: near-zero memory waste, far higher GPU memory utilization, and the ability to serve many more concurrent requests on the same hardware. Vanilla Hugging Face Transformers wastes 60-80 percent of KV cache memory on padding, vLLM uses over 96 percent of it. This is the core reason vLLM achieves 10x to 24x higher throughput than Transformers for the same model on the same GPU.

Continuous batching (also called dynamic batching) is vLLM's request scheduling strategy that dramatically improves multi-user throughput. Traditional static batching processes a fixed batch of requests together and waits for the slowest one to finish before starting the next batch. Continuous batching processes requests at the iteration level, completed requests immediately leave the batch and new requests immediately fill the freed slots, with no waiting between batches. The GPU stays utilized continuously instead of idling. Combined with PagedAttention, continuous batching is the second pillar of vLLM's throughput advantage. For a typical production deployment serving multiple concurrent users, continuous batching alone provides 3x to 5x throughput improvement over static batching.

VRAM is the primary constraint for vLLM workloads because model weights plus KV cache must fit in GPU memory. For inference on 7B-13B models, NVIDIA GeForce RTX 5090 32GB is excellent value. For 70B models with AWQ or GPTQ 4-bit quantization, or multi-user serving of 13B-30B models, NVIDIA RTX PRO 6000 Blackwell 96GB provides the most VRAM available in a workstation GPU plus ECC memory. For 70B+ full-precision inference or high-concurrency serving, multi-GPU configurations (2x or 4x RTX PRO 6000 Blackwell) enable tensor parallelism via vLLM's tensor-parallel-size flag. NVIDIA CUDA is required, vLLM does not support AMD or Intel GPUs natively (though some forks exist). For data center deployments, H100, H200, and B100/B200 are the enterprise choice.

vLLM VRAM requirements equal model weights + KV cache for active sequences + working memory. Rough formula: at FP16, a 7B model uses about 14GB for weights, 13B uses 26GB, 70B uses 140GB. Add 4-12GB for KV cache depending on max sequence length and batch size (vLLM dedicates a large portion of remaining VRAM to KV cache via gpu-memory-utilization flag). With 4-bit AWQ or GPTQ quantization, divide weights by 4 (70B fits in 35GB plus KV cache). Practical guide: an RTX 5090 32GB runs 13B at FP16 with multi-user batching, or 30B at Q4. An RTX PRO 6000 96GB serves a 70B at Q4 with hundreds of concurrent requests, or a 30B at FP16 with high throughput, or hosts 13B with very long contexts (32K+ tokens).

vLLM is the production default for most teams: easiest setup (pip install vllm), OpenAI-compatible API, supports virtually every model on Hugging Face Hub, dramatic throughput improvements over vanilla Transformers, active development. Hugging Face Text Generation Inference (TGI) is similar with slightly simpler deployment via Docker, popular for Hugging Face-native shops. NVIDIA TensorRT-LLM offers the highest throughput numbers but requires per-model compilation (each model must be converted to a TensorRT engine), more complex deployment, and is best for very high-volume enterprise serving where the compilation overhead is worth the throughput gains. For most production workloads, vLLM is the sweet spot. Most teams start with vLLM, then move to TensorRT-LLM only if extreme throughput becomes necessary. All three run on the same NVIDIA hardware.

vLLM serves one base model per engine instance, but supports multi-LoRA serving: a single base model with multiple LoRA adapters loaded dynamically and switched per request. This is the standard pattern for production multi-tenant serving where each customer has their own fine-tuned LoRA on top of a shared base model. For completely different base models, run separate vLLM processes on different GPUs (or split a GPU with MIG on supported hardware). For mixing inference and training workloads on the same hardware, dedicate GPUs to each, vLLM expects exclusive GPU access for predictable latency. VRLA Tech AI workstations support all these configurations.

For vLLM workloads, CPU needs are modest compared to training rigs because inference is GPU-bound, the CPU's job is request orchestration, tokenization, and API serving. For single-GPU vLLM serving, AMD Ryzen 9 9950X with 16 cores is more than sufficient. For multi-GPU tensor parallelism, AMD Threadripper PRO (9970X 32 cores or 9985WX 32 cores with 128 PCIe Gen5 lanes) ensures full PCIe bandwidth per GPU. AMD EPYC offers more cores and PCIe lanes for high-concurrency API serving. System RAM should be 1.5x to 2x your VRAM for caching tokenized inputs, request queues, and OS overhead. ECC is recommended for production deployments running 24/7. Fast NVMe storage matters for model weight loading (a 70B model is 140GB to load at startup).

Cloud GPU instances for vLLM-grade hardware (A100, H100, H200) run $2-$8 per GPU-hour. For production LLM serving where the workload is 24/7, that adds up rapidly to hundreds of thousands of dollars per year. A dedicated vLLM workstation gives you predictable fixed-cost compute with no per-token cloud markup, full data sovereignty for sensitive workloads (financial, healthcare, legal, government), no API rate limits, no egress fees on responses, no surprise pricing changes, and consistent latency that does not vary with cloud noisy-neighbor effects. Cloud remains useful for bursty traffic spikes or geographic distribution. For most internal LLM serving and B2B applications, an on-prem vLLM workstation pays back in weeks. Browse configurations at vrlatech.com.

The best vLLM workstation in 2026 prioritizes high-VRAM NVIDIA Blackwell GPUs (for model and KV cache capacity), Linux Ubuntu LTS as the OS, fast NVMe storage for model loading, and adequate ECC system RAM. For small to mid-size models (7B-13B), a single NVIDIA RTX 5090 32GB build is the cost-effective entry point. For production 70B serving and multi-user high-concurrency workloads, VRLA Tech recommends 1-2× NVIDIA RTX PRO 6000 Blackwell 96GB paired with AMD Threadripper PRO 9985WX and 256-512GB DDR5 ECC RAM. For very high-throughput serving across multiple models, 4× RTX PRO 6000 Blackwell with tensor parallelism and 1TB ECC RAM. Browse all configurations at vrlatech.com/vrla-tech-workstations/ai-deep-learning-workstations-high-performance-computing.

VRLA Tech designs and hand-assembles custom vLLM inference workstations, LLM serving rigs, and GPU servers in Los Angeles. Browse AI and deep learning workstation configurations at vrlatech.com/vrla-tech-workstations/ai-deep-learning-workstations-high-performance-computing. Every system ships with vLLM, PyTorch, CUDA, cuDNN, Hugging Face Transformers, and the full inference stack pre-installed and version-matched, a 3-year parts warranty, and lifetime US-based engineer support from engineers who specialize in HPC and AI workflows. Trusted by enterprise teams, federal research labs including Los Alamos National Laboratory, and universities including Johns Hopkins and George Washington University.