The gap in kv cache efficiency vllm sglang tgi directly separates a fleet that serves 10k req/s from one that falls over at 500. These three open-source serving stacks take fundamentally different approaches to memory management for transformer attention state, and the differences show up in throughput, tail latency, and GPU burn.
Capabilities and KV cache mechanisms
vLLM
vLLM pioneered PagedAttention: it splits the KV cache into fixed-size blocks mapped via a block table, exactly like an OS virtual memory manager. This eliminates the contiguous-allocation waste that kills throughput under variable sequence lengths. Since v0.4 it ships --enable-prefix-caching, which hashes token prefixes and reuses identical blocks across independent requests. That makes it competent at shared-system-prompt workloads without bespoke plumbing.
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3-8B \
--enable-prefix-cachingSGLang
SGLang ships RadixAttention. Every prompt prefix is stored in a radix tree shared across all concurrent requests; cache hits are automatic and require zero client cooperation. The framework also exposes a Python DSL for structured generation, so the same radix tree backs multi-call LLM programs (e.g., branch + merge). It is the most aggressive design for cross-request reuse.
python -m sglang.launch_server \
--model-path meta-llama/Llama-3-8B \
--port 30000TGI
Text Generation Inference (TGI) focuses on production stability for Hugging Face models. It uses continuous batching and FlashAttention, but its KV cache is allocated per sequence with a pre-negotiated max length. Recent versions added limited prompt caching, yet it lacks a radix tree or block-sharing layer that spans unrelated requests. For single-tenant, low-fan-out traffic this is fine; for prefix-heavy multi-tenant traffic it leaves GPU memory on the table.
docker run --gpus all -p 8080:80 \
ghcr.io/huggingface/text-generation-inference:latest \
--model-id meta-llama/Llama-3-8BCost model
KV cache efficiency dictates how many concurrent sequences fit on a given VRAM budget. vLLM and SGLang both drive utilization near the physical limit: wasted cache fragments are either paged out (vLLM) or deduped (SGLang). That translates to fewer GPUs per 1k req/s, i.e., lower $/token. TGI’s static allocation forces you to size for the worst-case sequence length across the batch, so you routinely provision 20–40% more memory than the working set needs. None of these are paid services; the cost model is purely infrastructure overhead.
Latency and throughput characteristics
Under homogeneous prompts (same system prompt, different user suffix), SGLang wins on both p50 and p99 because the radix tree serves the prefix from cache and only the divergent tail hits the GPU compute path. vLLM with prefix caching closes much of the gap but pays a hash-lookup and block-reassembly cost. TGI shows flat latency until the batch fills, then degrades sharply because uncached prefixes consume the same memory as fresh ones.
For long-tail mixed traffic (random documents, RAG chunks), vLLM’s paged allocator gives the best general-purpose throughput. SGLang still helps when many retrievers hit the same wiki article, but its advantage shrinks. TGI remains predictable if you cap concurrency conservatively.
Ergonomics and operational feel
vLLM is a Python package with an OpenAI-compatible HTTP server. You can patch the scheduler in a afternoon. Metrics are Prometheus-compatible but bare-bones.
SGLang introduces a server plus a client DSL. The radix cache is on by default; tuning means setting --radix-cache-capacity and watching eviction logs. The operational surface is larger, but the payoff is real for programmatic workloads.
TGI is Docker-first, written in Rust + Python. It ships readiness probes, OpenTelemetry traces, and a health endpoint that actually works behind Kubernetes. If you want a server that does not need a custom sidecar, TGI is the least fuss.
Ecosystem and integration
vLLM supports the widest model matrix (Llama, Mistral, Qwen, Phi, and most custom arches via plugin). Its OpenAI endpoint drops into existing SDKs.
SGLang targets the SGLang DSL and OpenAI-compatible endpoints, but some features (e.g., constrained decoding) only work through its native client. Model support trails vLLM slightly, though all major HF models are covered.
TGI is the native Hugging Face runtime. Any model with a transformers implementation and supported quantizer (GPTQ, AWQ, bitsandbytes) works out of the box. It is the path of least resistance if your org already lives in the HF hub.
Limits and failure modes
vLLM’s prefix cache uses a simple hash; a single token difference early in the prompt invalidates the whole prefix. Block size tuning matters: too small = overhead, too large = fragmentation. Under extreme concurrency the block table itself becomes a lock-contention point.
SGLang’s radix tree needs an eviction policy. If you serve unbounded distinct prefixes, the cache grows until OOM unless you set a cap, after which you silently lose hits. The DSL also adds a learning curve and occasional version drift between client and server.
TGI’s limit is the memory ceiling per replica. There is no transparent way to share a 2k-token system prompt across 500 connections; you pay for it 500 times.
Head-to-head comparison
| Dimension | vLLM | SGLang | TGI |
|---|---|---|---|
| KV cache approach | PagedAttention + opt-in prefix blocks | Radix tree, automatic prefix sharing | Per-sequence contiguous, limited caching |
| Cross-request reuse | Yes (hash-based) | Yes (full radix) | Minimal |
| Best throughput profile | Mixed / long-tail traffic | Shared-prefix / LLM programs | Single-tenant steady batch |
| Operational complexity | Low (Python) | Medium (DSL + server) | Low (Docker, k8s-ready) |
| Model support | Broadest | Major HF models | HF-native, quantizers built-in |
| Maturity | High | Medium-high | High, production-hardened |
Which to choose
High-concurrency RAG or multi-tenant API with repeated context
Pick SGLang. The radix cache turns a 1k-token system prompt plus retrieved doc into a one-time cost. If you already write LLM programs with branching, the DSL is a bonus.
General-purpose OpenAI-compatible endpoint with heterogeneous traffic
Pick vLLM. Enable prefix caching, tune block size, and you get near-SGLang efficiency on hot prefixes while keeping the flexibility to serve random inputs. When fronting these with an OpenAI-compatible gateway such as n4n.ai, forward provider cache-control hints so the framework’s prefix cache is actually exploited by downstream clients.
Locked to Hugging Face ecosystem, need stable container and observability
Pick TGI. You sacrifice some KV cache efficiency, but gain a hardened Rust server, native metrics, and zero model-conversion friction. For low-fan-out internal tools the wasted VRAM is negligible.
Research or rapid prototyping on weird architectures
vLLM’s plugin model and active PR queue make it the fastest way to get a new model serving with decent cache behavior.
No framework wins outright. Measure your own prompt distribution before committing GPUs; the kv cache efficiency vllm sglang tgi gap only matters relative to how often your requests actually share prefixes.