vLLM vs SGLang vs TensorRT-LLM Errors & Serving Triage
High-throughput serving engines like vLLM, SGLang, and TensorRT-LLM experience distinct failure modes ranging from block allocation deadlocks to RadixAttention eviction stalls and MPI process aborts. This guide compares error topologies across the three leading runtimes.
Comparison Matrix
| Dimension | vLLM | SGLang | TensorRT-LLM |
|---|---|---|---|
| KV Cache Management | PagedAttention block allocator with static memory fraction configuration. | RadixAttention tree-based prefix caching with automatic eviction and reference counting. | Pre-allocated static workspace memory pool managed by C++ runtime engine. |
| Common Failure Signatures | No available memory for the cache blocks; request queue starvation. | Radix cache corruption, tree mismatch, or scheduler batching timeout. | EngineCore exception, CUDA graph replay failure, or tensor parallel synchronization abort. |
| Worker & Subprocess Architecture | Ray-managed worker processes or multiprocessing spawn backend. | Asyncio event loop with ZMQ / Ray multi-process workers. | MPI / torch.distributed rank orchestration with native C++ execution runtime. |
| Debugging & Telemetry Surface | Python traceback, Prometheus serving metrics, and debug block logs. | SGLang runtime inspector, router logs, and token state dumps. | TensorRT engine build logs, profiler JSON, and C++ stack traces. |
| Typical Recovery Protocol | Adjust --gpu-memory-utilization, restart vLLM server, or drop max block count. | Clear Radix cache, tune eviction thresholds, or restart router daemon. | Recompile TensorRT engine plan, check CUDA version alignment, and reset MPI ring. |
Signature failures and first triage commands
vLLM PagedAttention OOM
ValueError: No available memory for the cache blocks. Try increasing gpu_memory_utilization.python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3-70b-Instruct --gpu-memory-utilization 0.90SGLang RadixAttention Eviction Stalls
RuntimeError: RadixCache eviction failed to release sufficient blocks for incoming batch request token length.python3 -m sglang.launch_server --model-path meta-llama/Llama-3-70b-Instruct --mem-fraction-static 0.85TensorRT-LLM EngineCore Abort
Aborted (core dumped) [TensorRT-LLM Error]: EngineCore failed to synchronize tensor parallel rank 3.mpirun -n 8 --allow-run-as-root python3 run.py --engine_dir /engines/llama3Engineering decision guide
Choose vLLM for General OpenAI-Compatible Serving
vLLM provides robust PagedAttention, extensive model support, and reliable multi-node tensor parallelism with minimal configuration overhead.
Choose SGLang for Complex Multi-Turn Agentic Workflows
SGLang excels at structured generation and radix-based prefix caching, significantly accelerating multi-turn chat and JSON decoding pipelines.
Choose TensorRT-LLM for Maximum Raw Inference TPS
TensorRT-LLM offers the highest possible throughput and lowest latency through aggressive kernel fusion, but requires complex C++ engine compilation.
Frequently asked questions
Why does vLLM throw memory allocation errors even when GPU memory looks free?
vLLM reserves a static fraction of GPU memory on startup specified by --gpu-memory-utilization. If PyTorch activation memory or other processes consume headroom, allocation fails.
How does SGLang RadixAttention differ from vLLM PagedAttention?
RadixAttention builds a prefix tree in the KV cache to automatically share and reuse cached prompt prefixes across concurrent requests, whereas standard PagedAttention manages blocks per request.
What causes TensorRT-LLM MPI rank synchronization failures?
Rank aborts usually stem from mismatched input batch shapes across tensor-parallel ranks, corrupted engine plan binaries, or inter-node InfiniBand communication timeouts.