The vLLM vs TGI decision combines workload fit with maintenance policy. Both serving engines document continuous batching and paged attention; the choice cannot be reduced to one having a KV-cache feature the other lacks. For new deployments, TGI’s maintenance notice and archived repository are reasons to assess alternatives. For an existing service, migration still needs a compatibility and capacity check.
This comparison uses project documentation. It makes no measured throughput or latency claims. Pair the serving decision with self hosting LLM vs API cost and, for retrieval-heavy applications, the best vector database for RAG comparison. The generation engine is only part of the request’s cost and latency budget.
vLLM vs TGI: documented capabilities
| Decision | vLLM | TGI | What to establish for your service |
|---|---|---|---|
| Maintenance | Check the current repository releases and supported models | Maintenance mode since 2025-12-11; GitHub repository archived (read-only) since 2026-03-21, so no upstream fixes, security fixes included, can be merged | Whether the maintained release supports your required model and hardware; for TGI, how you will run a pinned, unpatched release |
| Request scheduling | Continuous batching | Continuous batching | Behavior with your mixture of short and long requests |
| Attention memory | PagedAttention and KV-cache management | Documents PagedAttention using kernels developed by the vLLM project | Cache capacity at your context length and concurrency |
| Client integration | Documents a compatible HTTP serving API | Documents streaming and a Messages API | Request fields, streamed events, finish reasons, and error behavior |
| Telemetry | Prometheus metrics including queue and latency signals | Documents Prometheus metrics | Which metric names and units your pinned version exports |
| Selection evidence | Project capabilities plus a workload-specific validation | Project capabilities, maintenance policy, and migration cost | Requests meeting both the quality bar and latency budget |
The TGI overview supplies the maintenance and feature statements, and the TGI GitHub repository carries the archive notice. Its PagedAttention documentation explicitly describes paged KV memory. The vLLM repository documents its serving capabilities. These are feature statements, not proof that either engine is faster for a particular application.
An archived repository changes the planning horizon
Hugging Face put TGI in maintenance mode on 2025-12-11 and archived the GitHub repository (read-only) on 2026-03-21, so no further upstream fixes, security fixes included, can be merged; plan existing deployments around a pinned, unpatched release. The maintenance notice directs readers toward other inference engines, and the archive removes any upstream path for a patch.
For a new service, assess vLLM against the model architecture, quantization format, accelerator, and client features you require. If a current TGI service meets those requirements, record the operational cost of keeping it and the work needed to move. Avoid treating a migration as only a container-image substitution. To broaden the shortlist beyond these two engines, compare LLM serving frameworks against the same workload requirements.
The broader LLMOps tools on GitHub map puts repository health checks beside the gateway, retrieval, and release tools. Keep a dated dependency inventory so a maintenance-policy change has an owner and a response.
Compare goodput under a latency budget
A useful comparison asks how much completed work meets the service’s latency objectives. Define the unit before calculating goodput: successful requests per second is useful for user-facing tasks, while output tokens per second can describe a fixed workload mix. Do not switch between them midway through a cost comparison.
For example, define request goodput as:
request_goodput =
successful requests meeting the latency objectives / elapsed secondsSet time-to-first-token and completion-time objectives from the application’s requirements. For streaming interfaces, also consider gaps between emitted tokens. A high aggregate token rate can coexist with unacceptable waits for some requests, so retain latency distributions alongside throughput.
Specify whether a “successful request” means transport success, schema-valid output, or an answer passing a task check. These are different denominators. Quality evaluation may happen later, but the cost report should state which denominator it uses. Token cost observability connects billed attempts and GPU capacity to completed work.
Hold the workload constant
Build a comparison matrix before changing engine settings. This is a suggested validation procedure, not a set of results from this site.
| Variable | Keep constant or report explicitly |
|---|---|
| Model | Exact weights revision, tokenizer, chat template, and precision |
| Hardware | Accelerator count, memory, interconnect, and CPU allocation |
| Requests | Input/output length distribution and arrival pattern |
| Generation | Sampling settings, stop conditions, and output limits |
| Cache state | Cold starts, warmed prefixes, and reuse pattern |
| Capacity | Replicas, concurrency limits, and admission policy |
| Outcomes | Errors, cancellations, retries, quality checks, and latency distribution |
Replay representative short conversations and long-document requests separately, then together. Report the mixed workload as its own case: an average across isolated cases does not describe contention between them.
Include sustained load and a burst scenario. Record how queues drain after the burst and whether failed requests are retried by a gateway. Otherwise, a server-side benchmark can omit work that users and billing still experience. Keep client-side timings as well as engine metrics, since network and gateway time also belong to the user request.
Read the metrics your version exports
The vLLM production metrics guide describes the Prometheus endpoint. The metrics design documentation describes queue time, time to first token, end-to-end latency, and KV-cache usage.
Metric names change between versions. Current documentation includes the KV-cache gauge named vllm:kv_cache_usage_perc; older deployments may expose different names. Inspect the pinned version’s endpoint before editing a dashboard. Avoid carrying a historical metric name into an alert without checking its type, units, and labels.
Read queue time alongside cache pressure and active requests. A growing queue alone does not identify the bottleneck. Compare it with the workload mix and resource signals before increasing concurrency: that change may admit more work while making the latency distribution worse. Use those signals as requirements when you compare serving observability tools.
Do not prescribe a universal healthy cache percentage. A cache gauge describes allocated blocks, and its useful operating range depends on available headroom and scheduling behavior. Establish the acceptable range from the service’s objectives and representative traffic.
A TGI-to-vLLM migration checklist
Treat the following as release acceptance criteria:
- Pin the target engine and model artifacts, including the tokenizer and chat template.
- Verify request compatibility: model identifiers, generation options, stop sequences, output constraints, and cancellation.
- Check streaming behavior with the actual client, including empty responses, partial streams, and errors.
- Compare output quality on the same fixtures used for the existing service.
- Validate capacity with retries and gateways included, keeping enough headroom for the rollback path.
- Translate dashboards using exported metric definitions, rather than renaming series by guesswork.
- Route a limited traffic cohort to the candidate and keep the previous release recoverable until acceptance criteria hold.
A rollback bundle should include serving configuration as well as weights. Reverting a container while leaving a changed chat template or routing rule active can produce a different service from the one being restored.
Retrieval changes the serving workload
A RAG request sends retrieved context into the generation engine. Changing chunk count, reranking, or truncation changes the prompt-length distribution used in capacity planning. A faster vector query can still increase total response time if it returns substantially more context to process.
Use the vector database comparison to define retrieval constraints, then validate generation with the resulting prompts. Keep retrieval and serving release identifiers together so a latency change can be traced to the correct layer.
Frequently asked questions
Does TGI support PagedAttention?
Yes. TGI’s official PagedAttention page documents that support. It is incorrect to attribute every vLLM/TGI performance difference to TGI lacking paged memory.
Is vLLM always faster than TGI?
The feature documentation does not establish a universal winner. Compare the same model, hardware, request distribution, quality target, and latency objectives. Maintenance status is a separate selection criterion.
Does a cheaper GPU instance guarantee cheaper hosting?
No. Effective cost depends on completed work, paid idle time, redundancy, and operating effort. Use the self-hosting cost breakdown for that calculation, and serverless vs dedicated GPU hosting for the capacity-shape decision.
