NVIDIA Tesla P40 · 24GB VRAM

vLLLM GPU 服务器

使用专用 NVIDIA GPU 硬件的 vLLM 提供最大传输量的大型语言模型。 从框中打开 OpenAI 兼容的 API 。

$ pip install vllm && vllm serve meta-llama/Llama-3-8B-Instruct --host 0.0.0.0
# 运行于 NVIDIA Tesla P40 (24GB)
准备就绪 。 _

GPU VPS上是什么?

vLLM 是一个高通量LLM 服务引擎,使用 PagedAttention 来高效的内存管理。 在 GPU VPS 上运行 vLLM 给您一个具有最佳性能的可生产性 LLM API 。

为什么在 VPS.org GPU 上vLLM

页面访问

高效的 GPU 存储管理,用于更高的输送量。

连续对接

处理多个同时提出的请求,并优化利用全球保护联盟。

O开放AI API

OpenAI API 端点的空置替换 。

示范支助

LLAMA、Mistral、Gemma、Quen和50+模型建筑。

流行 vLLM 使用案例

LLM APP 生产
高流量聊天机
批发文本处理
多租租客LLM服务
AI SaaS 后端
企业AIAI平台

GPU 指定

GPU 通用 GPUNVIDIA Tesla P40
卷内24 GB GDDR5X
CUDA核心3,840
FP3212 TFLOPS
INT847 TOPS
内存 BW346 GB/s
建筑结构Pascal (GP102)
被动通过巴巴金属多氯二苯基

常问问题

What is vLLM on a GPU VPS?

+

vLLM on a GPU VPS is a CUDA-accelerated deployment. vLLM is primarily an LLM-inference / chat workload. You will want fast random-access reads from disk to memory and enough VRAM for the model plus context window.

How do I set up vLLM on a GPU VPS?

+

Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install vllm && vllm serve meta-llama/Llama-3-8B-Instruct --host 0.0.0.0. Your vLLM environment is ready in minutes with full GPU acceleration.

How much VRAM do I need for vLLM?

+

LLM inference VRAM scales with model parameters. A 7B model needs ~5-8 GB VRAM, 13B ~10-14 GB, 70B requires multi-GPU or quantization. Our 24 GB Tesla P40 comfortably runs 7B-13B models at full precision and 30B-class models with INT8 quantization.

Is vLLM GPU VPS billed hourly or monthly?

+

GPU VPS plans are billed monthly with no lock-in contracts and can be cancelled anytime. Contact us for current GPU pricing tiers.

Can I run other tools alongside vLLM?

+

Yes — you have full root on the GPU VPS. Run whatever fits inside the 24 GB VRAM and the available RAM / storage budget alongside vLLM.

Do I get full root on the vLLM GPU VPS?

+

Yes. Full root SSH on every GPU VPS — install drivers, swap CUDA versions, customize the environment for vLLM however you need.

Which CUDA version is installed for vLLM?

+

GPU VPSs ship with a recent CUDA runtime and the matching NVIDIA driver pre-installed. You can pin or upgrade CUDA versions as required by your vLLM workload.

Does my vLLM GPU VPS persist between sessions?

+

Yes — your vLLM GPU VPS is a long-running persistent server, not an ephemeral instance. Models, configs, and data stay on the SSD between sessions.

Where should I store data for my vLLM workload?

+

Keep working data on the VPS SSD for fast access during vLLM runs; back up finished artifacts (weights, generations, embeddings) off-server via snapshots or object storage for safety.

Can I scale my vLLM GPU VPS later?

+

Yes — plan upgrades are instant from your control panel; the GPU itself can be swapped to a larger tier on request. Your vLLM install carries over.

Are backups available for my GPU VPS?

+

Yes. Automated daily backups are an add-on; manual snapshots are free. Useful for long vLLM training runs where you want a checkpointable server state.

Is there a money-back guarantee on the GPU VPS?

+

Yes — 30-day money-back guarantee on every plan including GPU. Try vLLM on a GPU VPS risk-free.

准备在 GPU 上运行 vLLM 吗?

在几分钟内部署专用的 NVIDIA GPU 服务器。 没有预约, 没有销售电话 。