SGLang vs AirLLM

Detailed side-by-side comparison to help you choose the right tool

SGLang

🔴Developer

LLM Inference

High-performance open-source serving framework for LLMs and multimodal models, optimized for structured generation and complex agent workloads.

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Starting Price

Custom

AirLLM

🔴Developer

LLM Inference

Layer-by-layer LLM inference library that lets a 70B model run on a 4 GB GPU, or a 405B model on 8 GB.

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Starting Price

Custom

Feature Comparison

Scroll horizontally to compare details.

FeatureSGLangAirLLM
CategoryLLM InferenceLLM Inference
Pricing Plans6 tiers6 tiers
Starting Price
Key Features

      SGLang - Pros & Cons

      Pros

      • RadixAttention is a real throughput win for agent loops with shared prefixes
      • Constrained decoding makes JSON/tool-call output cheap
      • Often leads vLLM on DeepSeek MoE and structured workloads
      • Apache 2.0 — no license cost, fully self-hostable
      • OpenAI-compatible API means most client SDKs work unchanged

      Cons

      • Operational complexity higher than vLLM
      • Smaller ecosystem of third-party guides and integrations
      • Parallelism sharding is unforgiving — misconfigurations hurt throughput badly
      • Smaller managed-service ecosystem than vLLM
      • Documentation assumes prior inference-serving experience

      AirLLM - Pros & Cons

      Pros

      • Runs frontier open-weights models (Llama-3.1 405B, Qwen, DeepSeek) on hardware most builders already own
      • Zero infrastructure — one pip install, no server or account
      • HuggingFace-compatible generate() API drops into existing scripts
      • 4-bit and 8-bit quantization plus checkpoint compression cut disk footprint
      • Apache 2.0 license, no telemetry, safe for air-gapped work

      Cons

      • Throughput is tokens-per-minute, not tokens-per-second — useless for interactive chat
      • No batching, no continuous serving, no OpenAI-compatible endpoint out of the box
      • Weight-streaming means first-token latency scales with disk/RAM speed
      • Not a production serving stack — pair with vLLM or KTransformers for real traffic
      • Limited documentation compared to the mainstream inference frameworks

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