DeepInfer vs vLLM
The broadest comparison: a one-model DeepSeek engine vs a widely adopted general serving framework. API, model coverage, and maturity decide it.
Read comparison →FIG. 05 — Comparison desk / 2026.10
DeepInfer vs vLLM, SGLang, and TensorRT-LLM
Three source-based comparisons, one question: which serving layer should run DeepSeek-V4.1-Flash? We compare scope, hardware, API surface, cache architecture, and maturity — not invented speed numbers.
Comparisons3vLLM · SGLang · TensorRT-LLM
MethodScope + fitnot a speed contest
Head-to-headNone publicas of 2026-10-20
Hands-onNot testedsource-based
Snapshot2026-10-2011:30 UTC
Read this first
These are comparisons of scope, not a race.
No fabricated benchmark
DeepInfer is not a drop-in replacement for a general serving framework. It is DeepSeek's Apache-2.0 inference engine for one model, V4.1-Flash, on a narrow hardware list. vLLM, SGLang, and TensorRT-LLM are serving stacks with broader model coverage, more complete serving layers, and production features DeepInfer's release does not include.
That makes “which is faster?” the wrong first question. The useful questions are: which models do you need, what hardware do you have, do you need an API today, and who owns the KV cache and routing layer?
Our comparison rule
We compare published scope and architecture. We do not declare a speed winner. There is no public head-to-head for DeepInfer vs vLLM, SGLang, or TensorRT-LLM on identical model, hardware, context, and workload. If you see a page that claims one, check its test conditions first.
| Engine | Type | V4.1-Flash | API layer | KV cache / PD | Hardware | Best fit |
|---|---|---|---|---|---|---|
| DeepInfer | DeepSeek-specific offline engine | Yes, the only model | No online API | KV cache, PD, LB not included | SM100, Ascend 950 | Research, custom V4.1 deployment |
| vLLM | General LLM serving | Published optimizations | OpenAI-compatible server | Prefix cache, PD available | Broad, including Ascend plugins | General API, broad models |
| SGLang | General high-performance serving | Preview recipe | OpenAI-compatible server | Prefix cache, HiCache, PD | NVIDIA Blackwell/Hopper, AMD MI350X | Complete V4.1 serving path (preview) |
| TensorRT-LLM | NVIDIA serving stack | Check support matrix | trtllm-serve, Triton, Dynamo | KV cache, PD, Wide EP | NVIDIA GPUs | NVIDIA-standardized production |
Table is a scope summary, not a benchmark. Sources are linked on each comparison page.
The three comparisons
Pick the one that matches your decision.
Updated 2026-10-20
DeepInfer vs SGLang
SGLang is the closest comparison because it already has a V4.1-Flash recipe — preview images, DSpark, PD disaggregation, HiCache, and a router included.
Read comparison →DeepInfer vs TensorRT-LLM
NVIDIA's general inference stack vs DeepSeek's V4.1-specific engine. Hardware lock-in, API surface, and engine-build complexity are the trade-offs.
Read comparison →Method
What we compare — and what we refuse to make up.
EEAT / source-based
WE COMPARE
Scope and fit
Model support, hardware, API surface, cache architecture, PD disaggregation, license, and maturity. These are checkable from public docs.
WE DON'T
Declare a speed winner
No public same-model, same-hardware, same-workload benchmark exists. We do not turn vendor charts into a leaderboard.
WE LINK
Every claim
Official READMEs, cookbooks, model cards, and vendor blogs. If a number is vendor-reported, we say so. If we haven't run it, we say that too.
A note on “VS” pages
A comparison page is useful when it helps you decide. It is harmful when it invents a winner. These pages are written for the first case: they tell you where each engine fits, what it costs in engineering, and what evidence is still missing.
Evaluation plan
If you ignore the vendor charts, test like this.
7 steps
| # | Step | What to pin down |
|---|---|---|
| 1 | Model | Exact checkpoint (for example V4.1-Flash-1017), quantization, tokenizer, and prompt encoding. |
| 2 | Engine | Commit or image digest, build flags, CUDA/CANN version, and every environment variable that changes kernels or scheduling. |
| 3 | Hardware | GPU/NPU model and count, interconnect, memory, and whether it is one node or a cluster. |
| 4 | Workload | Real input/output distribution, turns per session, concurrency, and prefix-cache hit rate. |
| 5 | Latency | TTFT and TPOT at p50/p95/p99, plus error and timeout rates under the same SLO. |
| 6 | Throughput | Tokens/s under a stated latency constraint — not a peak at unlimited concurrency. |
| 7 | Cost | Cost per million delivered output tokens, including utilization, energy, storage, retries, and idle capacity. |
This checklist is our method, not a vendor benchmark. It applies to DeepInfer, vLLM, SGLang, and TensorRT-LLM equally.
Go deeper
The evidence behind the comparisons.
Source pages