SGLang vs AirLLM
Detailed side-by-side comparison to help you choose the right tool
SGLang
🔴DeveloperLLM Inference
High-performance open-source serving framework for LLMs and multimodal models, optimized for structured generation and complex agent workloads.
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CustomAirLLM
🔴DeveloperLLM 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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CustomFeature Comparison
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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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