By VRLA Tech  ·  AI Computing  ·  April 2026

vLLM has become the de facto inference engine for teams deploying open-weight models in production. It runs on cloud instances, it runs on workstations, it runs on dedicated servers. The framework itself is hardware-agnostic. But the experience of running it on cloud versus dedicated on-premise hardware is not the same — and most guides skip the parts that actually matter once you move beyond a single-GPU demo.

This post covers what actually changes when you move vLLM from a cloud instance to your own hardware: performance, setup, operational considerations, and what the numbers look like when you run the math properly.

What vLLM is and why it matters for on-premise deployment

vLLM is an open-source inference engine built around PagedAttention — a technique borrowed from operating system memory management that treats GPU VRAM like virtual memory. Instead of reserving one large contiguous block of memory per request (which wastes 60–80% of GPU capacity under typical serving conditions), PagedAttention breaks the KV cache into fixed-size blocks that can be stored anywhere in GPU memory and reused across requests.

The result is dramatically higher throughput at equivalent hardware cost. For production serving where you are handling many concurrent requests, this matters enormously. vLLM v0.15.1 (February 2026) added full NVIDIA Blackwell SM120 support and H200 optimizations, making it the most capable version yet on current hardware generations.

In 2026 the inference engine landscape has expanded. SGLang delivers about 29% higher throughput than vLLM on H100s for multi-turn workloads (roughly 16,200 vs 12,500 tokens per second), and LMDeploy leads on quantized model serving with its C++ TurboMind engine. But vLLM remains the most mature ecosystem with the broadest hardware support, the largest community, and the most model compatibility — making it the safe default for teams that want production stability over maximum throughput.

Cloud vLLM: what you actually get

Running vLLM on a cloud GPU instance is straightforward. Spin up an instance, install the package, point it at a model weight directory, and you have an OpenAI-compatible inference endpoint in minutes. For proof of concept work and early-stage experimentation this is genuinely useful.

The problems emerge at scale. Cloud GPU instances add latency that does not exist on dedicated hardware — network hops, virtualization overhead, and shared infrastructure all contribute. More significantly, cloud instances are subject to availability constraints. During peak demand, H100 and H200 instances queue. Your inference endpoint goes from milliseconds to minutes of wait time simply because another tenant needed the same resources.

There is also the memory configuration problem. vLLM’s --gpu-memory-utilization parameter defaults to 0.9 (90% of VRAM). On a cloud instance with shared GPU memory or variable VRAM allocation, this number is unreliable. Teams frequently encounter out-of-memory crashes on cloud instances that would not occur on dedicated hardware with the same nominal VRAM specification, because the actual available memory on a shared instance differs from the advertised spec.

On-premise vLLM: what actually changes

On dedicated hardware, vLLM behaves exactly as designed. You have the full VRAM specification, no virtualization overhead, and no competing tenants. The --gpu-memory-utilization flag behaves predictably. Your KV cache is sized correctly. Your throughput is consistent.

The practical differences for a team running production vLLM on a VRLA Tech EPYC LLM Server with 4× NVIDIA RTX PRO 6000 Blackwell Max-Q:

Hardware sizing for on-premise vLLM

The most common question when moving vLLM on-premise is how much hardware you actually need. The answer depends entirely on which models you are serving and at what concurrency.

7B–13B models (LLaMA 3.1 8B, Mistral 7B, Qwen2.5 7B)

A single NVIDIA RTX PRO 6000 Blackwell with 96GB VRAM runs these models in full FP16 with substantial KV cache headroom for high concurrency. You could run two or three of these models simultaneously on a single GPU. This is the entry point for most teams moving from a workstation to a dedicated server.

32B–70B models (LLaMA 3.1 70B, Qwen2.5 72B, DeepSeek models)

70B models in FP16 require approximately 140GB of VRAM. A 2-GPU configuration with tensor parallelism handles this comfortably. A 4-GPU configuration leaves substantial headroom for KV cache, supporting higher concurrency and longer context windows. VRLA Tech’s 2U LLM server with 4× RTX PRO 6000 Blackwell delivers 384GB total VRAM — enough for full FP16 inference on 70B models with room to spare.

150B+ models (LLaMA 3.1 405B, large MoE models)

Foundation model training and inference at this scale requires the 4U 8-GPU configuration with over 1.1TB combined VRAM. vLLM’s tensor and pipeline parallelism handles multi-GPU inference across all 8 cards transparently.

Setup: on-premise vLLM is simpler than you think

One common objection to on-premise vLLM is setup complexity. In practice, on a properly configured dedicated server, it is straightforward. Every VRLA Tech LLM server ships with CUDA drivers, Python environment, and vLLM pre-installed and validated. You plug it in, connect it to your network, and run:

vllm serve meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.92 \
  --max-model-len 32768

That starts a production-ready OpenAI-compatible endpoint. Your existing code that calls the OpenAI API works without modification — just point the base URL at your server’s IP address.

The engineering overhead of managing cloud vLLM — instance lifecycle, spot interruption handling, auto-scaling configuration, networking, cost monitoring — disappears entirely with dedicated hardware. The server runs. vLLM serves. You focus on your models and your application.

The framework landscape in 2026

vLLM is not the only option and the gap between frameworks has narrowed significantly. SGLang now runs on over 400,000 GPUs worldwide and delivers 29% higher throughput on H100s for multi-turn workloads. Text Generation Inference entered maintenance mode in December 2025. LMDeploy’s TurboMind C++ engine delivers the lowest latency for quantized model serving.

VRLA Tech LLM servers ship validated for all major inference frameworks — vLLM, SGLang, TensorRT-LLM, and TGI — so you are not locked into one choice. As the ecosystem evolves, your hardware supports whatever framework delivers the best results for your workload.

VRLA Tech has been building custom AI workstations, GPU servers, and HPC systems since 2016. All systems ship with a 3-year parts warranty and lifetime US-based support.