Configuraciones recomendadas

vLLM is a production-grade LLM inference server. PagedAttention, continuous batching, and tensor parallelism deliver 10–24x higher throughput than naive HuggingFace inference. OpenAI-compatible API. GPU required.

Development — small models

7-13B models, dev/staging API testing and development use
desde €199.00/mo
Dedicated server
RTX 4090 (24 GB VRAM)
CPU
6 cores
RAM
32 GB RAM
Almacenamiento
100 GB NVMe
Red
1 Gbps unlimited
24–72h

Ideal for serving 7B–13B models in production

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Enterprise — multi-GPU

Maximum throughput, tensor parallelism Enterprise-grade inference cluster
desde €1,199.00/mo
Dedicated server
2× A100 (160 GB VRAM)
CPU
16 cores
RAM
256 GB RAM
Almacenamiento
500 GB NVMe
Red
1 Gbps unlimited
24–72h

Tensor parallelism across multiple GPUs

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¿Buscas una configuración GPU específica?

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Por qué vLLM necesita el servidor adecuado

PagedAttention multiplies throughput

vLLM's PagedAttention manages GPU memory like virtual memory in an OS, allowing efficient KV cache reuse. This delivers 10–24x higher throughput than running models directly with HuggingFace Transformers.

OpenAI drop-in replacement

vLLM exposes an OpenAI-compatible API. Change one environment variable in your application (the base URL) and your app runs against your own model instead of paying per token.

Supports all major open models

Llama 3, Mistral, Mixtral, Qwen, DeepSeek, Gemma — vLLM supports all major model architectures. Pull any model from HuggingFace Hub and serve it with vLLM without code changes.

Unlimited bandwidth critical

High-throughput LLM serving generates significant outbound traffic. Bandwidth caps will limit your API throughput and add unpredictable costs. All Dedimax plans include unlimited traffic.

Preguntas frecuentes

What makes vLLM better than running models directly?

vLLM's PagedAttention and continuous batching allow it to serve many concurrent requests efficiently. Running a model directly with HuggingFace processes one request at a time. vLLM can batch dozens of requests simultaneously, achieving 10–24x higher throughput.

Which GPU do I need for vLLM?

For 7–13B models: RTX 4090 (24 GB VRAM). For 70B models: A100 (80 GB VRAM). For multi-GPU tensor parallelism: 2× A100 or more. vLLM requires CUDA-compatible NVIDIA GPUs — consumer and data center GPUs both work.

Which models does vLLM support?

vLLM supports all major open model families: Llama (Meta), Mistral, Mixtral, Gemma (Google), Qwen (Alibaba), DeepSeek, Yi, Falcon, and more. Any model with a supported architecture on HuggingFace Hub can be loaded and served.

Can I use vLLM as a drop-in replacement for OpenAI?

Yes. vLLM implements the OpenAI REST API spec. Change the base URL in your application from api.openai.com to your server, and your existing code works with your self-hosted model. No SDK changes required.

How much bandwidth does vLLM consume?

It depends on your traffic. A server handling 100 requests/minute with average 1,000 token responses generates significant outbound data. Bandwidth caps will limit throughput and add costs. All Dedimax plans include unlimited traffic.

vLLM is the leading open-source LLM inference framework for production deployments. Its PagedAttention memory management and continuous batching deliver 10–24x higher throughput compared to naive inference, making it the choice for teams that need to serve LLMs at scale. vLLM exposes an OpenAI-compatible API — existing applications that call GPT-4 can switch to your self-hosted model by changing a single URL. For 7–13B models, an RTX 4090 with 24 GB VRAM provides a cost-effective starting point. For 70B models and production traffic, an A100 with 80 GB VRAM is the standard deployment target.

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