Key features
- PagedAttention — APAC GPU memory management for 3-5x more concurrent requests vs HuggingFace inference
- Continuous batching — dynamic APAC request batching for maximum GPU utilisation at variable load
- OpenAI-compatible API — drop-in replacement endpoint for APAC OpenAI API calls
- Multi-GPU serving — tensor and pipeline parallelism for APAC 70B+ model deployment
- Model quantization — AWQ, GPTQ, INT8, FP8 for APAC smaller GPU deployment with quality trade-offs
- Streaming generation — APAC streaming token output via Server-Sent Events
- APAC model support — Llama, Mistral, Qwen, Gemma, Doubao, EXAONE, HyperCLOVA compatibility
Best for
- APAC enterprises with data sovereignty requirements (MAS, HKMA, FSA, PDPA) that prevent sending customer data to cloud AI providers — vLLM enables production-quality LLM serving on APAC-controlled GPU infrastructure
- APAC platform engineering teams deploying open-weight models (Llama 3, Qwen 2.5, Doubao-1.5) at scale — vLLM's PagedAttention and continuous batching make GPU-efficient APAC LLM serving economically viable without proprietary cloud AI costs
- APAC AI product teams requiring OpenAI API compatibility for existing applications — vLLM's OpenAI-compatible endpoint enables migration from OpenAI to APAC self-hosted models without application code changes
- APAC enterprises deploying LLMs with APAC language requirements (Japanese, Korean, Mandarin, Indonesian) where multilingual open-weight models (Qwen 2.5, Doubao-1.5) are preferred over English-primary cloud models
Limitations to know
- ! GPU hardware requirement — vLLM requires APAC GPU infrastructure (NVIDIA A100, H100, A10G, RTX 3090+); APAC teams without existing GPU infrastructure must provision cloud GPU instances (AWS p4, Google A100, Azure NDv4) which add significant APAC operating costs
- ! Operational complexity — deploying and operating vLLM on APAC Kubernetes requires understanding GPU node pools, NVIDIA device plugins, CUDA dependencies, and model weight storage; APAC platform teams without ML infrastructure experience should invest in training before production vLLM deployment
- ! Memory-intensive — large APAC LLMs (70B parameters) require 140GB+ GPU VRAM at FP16; APAC enterprises without access to multi-GPU nodes must use quantization (reducing to 4-bit can enable 70B on 1x A100) which may degrade APAC language quality
- ! Model download and hosting — open-weight APAC models (Llama 3, Qwen 2.5, Doubao-1.5) range from 4GB to 400GB; APAC platform teams must provision sufficient storage for model weights and implement model version management for APAC production deployments
About vLLM
vLLM is an open-source LLM inference server developed at UC Berkeley that enables APAC enterprises to deploy open-weight language models (Llama 3, Mistral 7B/8x7B, Qwen 2.5, Gemma 2, Doubao-1.5, EXAONE, HyperCLOVA X) on APAC GPU clusters with dramatically higher throughput than naive Hugging Face Transformers inference — using PagedAttention memory management and continuous batching to achieve up to 24x higher throughput than standard LLM serving, making APAC production LLM deployment cost-efficient at scale.
vLLM's PagedAttention algorithm — where GPU memory for LLM KV (key-value) cache is managed using virtual memory techniques inspired by OS page tables, allocating non-contiguous GPU memory blocks for different request KV caches and enabling efficient memory sharing between concurrent APAC requests — solves the memory fragmentation problem that limits GPU utilisation in naive LLM inference: standard LLM serving allocates contiguous KV cache memory per request based on maximum sequence length, wasting GPU memory for shorter actual completions; PagedAttention allocates KV cache blocks dynamically based on actual token generation, enabling vLLM to serve 3-5x more concurrent APAC requests on the same GPU hardware compared to standard HuggingFace inference.
vLLM's continuous batching model — where incoming APAC inference requests are dynamically batched at the iteration level (not the request level), allowing new APAC requests to be added to an active batch mid-generation as earlier requests complete, and completed sequences to be replaced by new APAC requests without waiting for the entire batch to finish — eliminates the GPU idle time that occurs in static batching when fast APAC requests wait for slow requests in the same batch to complete, significantly improving GPU utilisation and APAC throughput at variable request rates.
vLLM's OpenAI-compatible API — where APAC applications interact with vLLM using the same `POST /v1/chat/completions` and `POST /v1/completions` endpoints, request format, and response structure as OpenAI's API — enables APAC engineering teams to deploy vLLM as a drop-in replacement for OpenAI API calls in APAC applications, redirecting API requests from `api.openai.com` to the APAC self-hosted vLLM endpoint without changing application code.
vLLM's multi-GPU and tensor parallelism support — where APAC platform teams configure vLLM to split large language model weights across multiple GPUs using tensor parallelism (splitting individual layer computations across GPUs for models that exceed single GPU memory) and pipeline parallelism (distributing layers across GPUs for multi-node APAC deployments) — enables APAC enterprises to serve 70B+ parameter language models (Llama 3.1 70B, Qwen 2.5 72B) across 4-8 A100/H100 GPUs in APAC data centres or cloud instances without model quantization that degrades APAC language quality.
Beyond this tool
Where this category meets practice depth.
A tool only matters in context. Browse the service pillars that operationalise it, the industries where it ships, and the Asian markets where AIMenta runs adoption programs.
Other service pillars
By industry