The Evolution of vLLM and Open-Stack Transparency

By early 2026, vLLM has solidified its position as the standard for high-throughput inference, largely due to its PagedAttention algorithm which solves the sequential memory bottleneck in KV caches. However, the release of NVIDIA Dynamo, which NVIDIA announced at GTC in March 2025, changed the orchestration layer. Unlike the proprietary engines used by many US-based providers, the open-stack combination of vLLM and Dynamo allows for deep visibility into the execution graph. This transparency is critical for engineers who need to debug performance regressions or implement custom kernels without being locked into a specific vendor's black-box stack.

When you deploy on Lyceum, you are utilizing this open-stack architecture. This means your models remain portable. If you need to move a workload from a dedicated H100 instance to a multi-GPU B200 cluster, the underlying container logic remains consistent. We prioritize this portability because vendor lock-in is a significant risk for scale-ups managing long-term infrastructure costs. Whether the open stack matches a proprietary engine on your workload is something to measure on your own traffic rather than take from a vendor benchmark.

  • Continuous Batching

    vLLM's ability to insert new requests into the batch as soon as a token is generated, rather than waiting for the entire batch to finish.

  • Quantization Support

    Native support for FP8 and INT8, which is essential for fitting larger models like Llama 3.1 405B onto standard GPU nodes.

  • OpenAI Compatibility

    The ability to use the OpenAI SDK as a drop-in replacement, requiring only a change to the base URL.

The primary advantage of this stack in 2026 is the integration of NVIDIA Dynamo, which adds disaggregated prefill and decode serving, KV-cache-aware request routing and dynamic scheduling of GPUs across an inference deployment. Provisioning the machines underneath is a separate concern, handled by the platform rather than by Dynamo, and without the manual block-reservations or unreliable auto-scaling groups of traditional hyperscaler workflows.

Memory Management: Solving the VRAM Fragmentation Crisis

The most common failure point in production inference is the Out-of-Memory (OOM) error caused by KV cache fragmentation. In traditional serving, VRAM is allocated statically, leading to 'internal fragmentation' where reserved space goes unused. vLLM's PagedAttention treats VRAM like virtual memory in an operating system, dividing the KV cache into blocks that can be stored in non-contiguous memory. This allows for near-zero waste, but it requires precise tuning of the gpu_memory_utilization parameter.

Setting this parameter to 0.90 is a reasonable starting point rather than a tuned recommendation; we have not published the model, workload and date that would make a firmer number meaningful. The parameter sets the fraction of VRAM capacity vLLM pre-allocates, so what it controls is capacity headroom, not memory bandwidth: the same fraction leaves far more absolute headroom on a 180 GB B200 than on an 80 GB H100. However, pushing too high without monitoring can lead to 'thrashing' if the model requires more space for activations than anticipated. This is where predictive scheduling helps. By estimating VRAM requirements from input sequence length and model architecture, Lyceum's scheduling product picks a suitable GPU for the workload instead of leaving the choice to a manual guess.

  1. Monitor Cache Usage

    Use the vLLM metrics endpoint to track avg_prompt_throughput and gpu_cache_usage_perc.

  2. Adjust Block Size

    While the default block size of 16 is standard, increasing this for long-context models (128k+ tokens) can reduce the overhead of the page table.

  3. Enable Speculative Decoding

    Use a smaller 'draft' model to predict tokens, which the larger 'target' model then verifies. This can cut latency noticeably at low batch sizes, though the gain shrinks as batch size grows.

A common mistake we see is dedicating an entire GPU instance to a model that only receives intermittent traffic. This leaves the GPU idle for most of the day and drives cluster utilization down. To combat this, we recommend a scale-to-zero strategy. By utilizing Lyceum's dedicated inference endpoints, you can configure your infrastructure to shut down when idle and spin back up when a new request arrives. This ensures you only pay for the compute you actually use, rather than maintaining a 'warm' instance 24/7 for a customer who only clicks a button once a day.

The Economics of Inference: Lyceum vs. Hyperscalers

For AI startups, the transition from hyperscaler credits to real-world billing is often a shock. Hyperscaler GPU pricing is frequently high for sustained inference. Lyceum provides H100 VMs at $2.79 per GPU-hour on-demand, and $3.59 per GPU-hour for dedicated inference and serverless training. That capacity runs in European data centres in Spain, Paris and the Nordics, billed per second with no base fee.

Infrastructure Cost Comparison

Hyperscaler GPU pricing is frequently high for sustained inference. Lyceum provides H100, A100 and B200 instances in European data centres. List prices per GPU-hour: H100 $2.79 on-demand VM and $3.59 dedicated inference, A100 $1.59 and $2.50, B200 $6.59 and $6.29. No long-term commitment is required.

Beyond the hourly rate, egress fees are the 'hidden tax' of AI infrastructure. Moving large datasets or model weights between regions can cost thousands of dollars on US-based clouds. Lyceum's S3-compatible storage carries no ingress or egress charges for your weights and datasets. This is particularly relevant for European teams who need to move data between different EU-based data centers for redundancy, though EU residency is not itself required by EU law and does not by itself make processing GDPR-compliant. Per-second billing is our standard, ensuring that if your testing session lasts 32 minutes and 14 seconds, you aren't billed for a full hour.

We also address the 'availability myth.' Many public clouds claim auto-scaling for GPUs, but in reality, they often fail to provision machines during peak demand, leading to long waits followed by a 'no capacity' error. Lyceum serves GPU workloads from European data centres in Spain, Paris and the Nordics, so when you need an H100 or a B200 cluster, it is provisioned through automated orchestration rather than a manual reservation. This reliability is why teams transitioning off expiring credits choose us as their long-term production partner.

Compliance as a Moat: GDPR and the EU AI Act

For any European enterprise, data residency is a non-negotiable requirement. US-based providers, even those with 'European regions,' are often subject to the US Cloud Act, which can create legal uncertainty for sensitive data. If you are working in healthcare, defense, or manufacturing, your customers will demand proof that their data never leaves Europe. Lyceum is a European inference platform built for that requirement, served from European data centres in Spain, Paris and the Nordics. On certifications we are direct: Lyceum holds no ISO 27001 certificate, no SOC 2 report and no BSI C5 attestation today, and states no EU AI Act conformity position. What we do offer is GDPR-compliant processing in European data centres, no training on customer data, and a DPA with named sub-processors available on request.

The EU AI Act, which becomes increasingly relevant in 2026, places strict requirements on 'high-risk' AI systems, including transparency and data governance. Using a US-hosted black-box API makes it nearly impossible to audit the data flow or verify compliance. By using Lyceum's dedicated inference, the machine is exclusively yours. There is no shared tenancy, and customer data is never used to train models. This 'sovereignty by design' is a competitive advantage when selling your AI solutions to regulated European industries.

The requirement we hear most often from regulated European buyers is blunt: processing has to happen in Europe, and data must not run over American servers.

We hear this across discovery calls with European AI teams. The ability to point to a European-operated company with European infrastructure simplifies the procurement process and removes the 'compliance hurdle' that often stalls enterprise deals. We provide the legal and technical infrastructure so you can focus on the model logic.

Implementation: From Docker to Production API

Deploying vLLM on Lyceum is designed to be a low-friction process. If you already have a Docker image or a model on Hugging Face, you can be live in minutes. Our OpenAI-compatible API means you don't need to rewrite your application logic; you update the base_url in your SDK configuration. This drop-in replacement capability is essential for teams moving away from expensive US-based APIs.

import openai
 
 client = openai.OpenAI(
 base_url=LYCEUM_BASE_URL,  # the base URL shown in your Lyceum dashboard
 api_key="your_lyceum_key"
 )
 
 response = client.chat.completions.create(
 model="meta-llama/Llama-3.3-70B-Instruct",
 messages=[{"role": "user", "content": "Optimize this vLLM config."}]
 )

For teams needing more control, our VMs and Infrastructure product provides raw GPU access via SSH. This is the simplest way to get a GPU for custom workloads. You add your SSH key, and you have a Linux machine ready for your environment. We also provide 'Lyceum containers' - a standardized virtualization layer that provides unified metrics for GPU and memory utilization across our European fleet. This ensures a consistent developer experience regardless of the site your capacity runs in.

Common mistakes during implementation include neglecting cold start times when scaling to zero. While we have optimized container loading to keep cold starts short, latency-sensitive applications should maintain at least one 'warm' replica. Our auto-scaling logic allows you to set minimum and maximum replicas, using round-robin load balancing to distribute traffic effectively. This level of control is what separates a production-grade deployment from a simple prototype.

Sources

[1] vLLM Documentation: Production Metadata; [2] NVIDIA Developer: Introducing NVIDIA Dynamo, announced at GTC on 18 March 2025 (read 3 August 2026); [3] arXiv: Efficient Memory Management for Large Language Model Serving with PagedAttention (vLLM); [4] NVIDIA DGX B200: 8 Blackwell GPUs, 1,440 GB total GPU memory, so 180 GB per GPU (read 3 August 2026)