Learn / Netra Runtime vs vLLM
Netra Runtime vs vLLM
Netra Runtime and vLLM both serve large language models, but they solve different problems. vLLM is a popular open-source inference engine that teams self-manage. Netra Runtime is a productized AI infrastructure platform with an OpenAI-compatible API, fine-tuning, and flexible deployment. In a published benchmark on the same 35B model and single NVIDIA H200 GPU, Netra served up to 4.4x more output per second than vLLM.
At a glance
| Dimension | Netra Runtime | vLLM |
|---|---|---|
| Type | Productized inference platform | Open-source inference engine |
| API | OpenAI-compatible | OpenAI-compatible server available |
| Deployment | On-prem, VPC, self-hosted, or hosted | Self-managed |
| Fine-tuning | Built-in | Bring your own pipeline |
| Operations | Productized, with Forward Deployed Engineers for enterprise | Self-managed |
| Best for | Teams wanting performance plus support | Teams building custom serving infra |
Benchmarked performance
In a published benchmark holding the model (a 35B model) and a single NVIDIA H200 GPU constant on a 32K-in, 16K-out long-context workload, Netra produced far more output per second than vLLM at every level of load. Words per second; higher is better.
| Users at once | Netra | vLLM | Netra vs vLLM |
|---|---|---|---|
| 1 | 987 | 225 | 4.4x |
| 8 | 2,444 | 1,092 | 2.2x |
| 32 | 5,514 | 1,931 | 2.9x |
| 128 | 6,582 | 2,230 | 3.0x |
| 256 | 7,111 | 2,372 | 3.0x |
When to choose which
Choose vLLM if you want a free, open-source engine and are happy to own and operate the serving stack yourself. Choose Netra Runtime if you want high performance together with an OpenAI-compatible API, built-in fine-tuning, flexible deployment (on-prem, VPC, self-hosted, or hosted), and operational support. Both are honest options; the right one depends on how much of the infrastructure you want to run.
Methodology
The benchmark above changed only the serving engine while holding the model, GPU, and workload constant, and also tested SGLang, SGLang+EAGLE, and SGLang+DFlash. Read the full benchmark post for the complete methodology and all engines tested.
Frequently asked questions
Is Netra Runtime a replacement for vLLM?
Netra Runtime can replace a self-managed vLLM deployment for teams that want a productized, high-performance platform with an OpenAI-compatible API, fine-tuning, and deployment support. vLLM remains a strong choice for teams that prefer to build and operate their own open-source serving stack.
How much faster is Netra Runtime than vLLM?
In a published benchmark on a 35B model and a single NVIDIA H200 GPU with a 32K-in, 16K-out workload, Netra served up to 4.4x more output per second than vLLM at a single request and about 3x more at 256 concurrent users, and streamed tokens roughly 5.5x faster. Results depend on model, hardware, and workload.
Does Netra Runtime use the same API as vLLM?
Both expose OpenAI-compatible APIs, so applications using OpenAI-style SDKs can target either by changing the base URL and key.