DeepInfer vs vLLM vs SGLang: Choose an Inference Engine by Deployment Needs

Compare what you can deploy

Scroll the table horizontally on a small screen to compare all columns.

These are selection starting points. A supported device family does not imply every model, quantization and kernel combination works.

DeepInfer: know the missing service pieces

The release README lists DeepSeek-V4.1-Flash, NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. It provides optimized execution and offline examples, but explicitly excludes a KV-cache system, full VE/prefill/decode disaggregation and load balancing across data-parallel replicas.

Its build prerequisites include Linux, Python 3.12, GCC 13 or later, and CUDA 13 or CANN 9.2 depending on accelerator. The published benchmark uses four SM100 GPUs for prefill and 64 for decode. Those conditions do not establish a laptop result or a matched win over another engine.

Choose it to investigate this specialized execution path. If you need a complete production API immediately, include the integration work in your decision rather than treating the offline example as an equivalent service.

vLLM: begin with model and API compatibility

The vLLM README documents continuous batching, prefix caching, distributed parallelism, structured outputs, tool/reasoning parsers and an OpenAI-compatible server. It also lists multiple hardware backends and many model architectures.

Choose it as an initial candidate when your application already expects a compatible API and your checkpoint has a documented serving path. Verify the exact model architecture, quantization and parser; protocol compatibility does not promise identical behavior to a hosted provider.

SGLang: follow the workload recipe

The SGLang README describes language, vision-language and diffusion inference, with use cases including agent workloads, large-scale serving and RL rollouts. Its platform guides distinguish NVIDIA, AMD, TPU, CPU, Apple Silicon and other paths; some integrations are still in progress.

Start with the official cookbook for the exact model and hardware. If your work spans modalities or an existing SGLang deployment, include that integration fit in the evaluation. A feature listed elsewhere in the ecosystem is not automatically enabled by one default server command.

Run a comparison that can answer your question

1. Freeze the inputs. Record checkpoint revision, tokenizer, precision, GPU count, interconnect, driver and engine commit.
2. Match the workload. Use the same input/output length distribution and concurrency. Keep cached-prefix and uncached runs separate.
3. Measure the user experience. Record time to first token, per-token latency and completed requests per second, including p50/p95 and errors.
4. Check correctness. Validate output formats, stop behavior, tool calls and task quality before accepting a throughput gain.
5. Include operations. Measure peak memory, restart behavior and the cost of running the whole service.

Report prefill and decode separately. If a configuration only wins by exceeding your latency or error target, it is not a win for that application. We have not run these engines or a cross-engine benchmark; no numeric performance winner is asserted.

Choose the smallest supported deployment

For a single supported checkpoint and a familiar API, test vLLM's serving path first. For a workload already covered by SGLang's recipes or ecosystem, compare that path using the same acceptance checks. Choose DeepInfer when its specific model/hardware scope fits and your team can own the remaining service components. These are editorial starting points, not universal rankings.

For a small local text model, see the separate Nemotron Bolt GGUF profile. For structured classification instead of generated text, use the Decision Models guide. The Decision 2.0 model family is not version 2.0 of the vLLM engine.