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RUNTIME / ADVANCED

vLLM

Linux GPU servers, concurrency and OpenAI-compatible production APIs

Sumber primer
DifficultyAdvancedSetup profile
Platforms3nvidia · amd · intel
APIOpenAI-compatible server:8000
01 / INSTALL & RUN

Installation depends on your OS. These commands are examples or templates: replace placeholders and verify quantization, file format, drivers and runtime support. Memory fit does not guarantee deployment.

  1. 1
    Installuv pip install vllm --torch-backend=auto
  2. 2
    Start a modelvllm serve <verified-checkpoint> --dtype auto
  3. 3
    Connect your app

    OpenAI-compatible server · :8000. Keep the service bound to localhost unless you add authentication and network controls.

Perhatian

CUDA, ROCm and XPU feature coverage differs. Driver, shared-memory and quantization compatibility are version-specific.

02 / COMPATIBLE MODELS

Model open-weight perwakilan

Alibaba Qwen

Qwen3.8 Flash Next

English
180BQ4 estimated budget
Estimasi memori
~108 GB
Konteks
262,144 native; 1M extended
inclusionAI

Ling-3.0-flash

English
124BQ4 estimated budget
Estimasi memori
~74.4 GB
Konteks
Check selected runtime and checkpoint
Alibaba Qwen

Qwen3.6 27B

27BQ4 / NVFP4 estimate
Estimasi memori
~18.5 GB
Konteks
262K native
Alibaba Qwen

Qwen3.8 27B

27BQ4 estimate
Estimasi memori
~19.5 GB
Konteks
262K native
Alibaba Qwen

Qwen3.6 35B-A3B

35B3B active · Q4 / NVFP4 MoE
Estimasi memori
~25 GB
Konteks
262K native
DeepSeek

DeepSeek V4 Flash

284B13B active · Official FP4 + FP8 mixed
Estimasi memori
~176 GB
Konteks
1M native