SilentSwarm or vLLM?¶
If your GPUs can hold the model and you have many users, use vLLM: it is built for raw throughput and is far more mature. SilentSwarm is for mismatched machines and models none of your cards can hold, and for fine-tuning across them.
Checked against vLLM's own documentation on 2026-11-05. Projects change, so check their current pages too. "Not documented" means we did not find it in what we read, not that it does not exist. We have not run either project against the other, so this page has no speed comparison.
| vLLM | SilentSwarm | |
|---|---|---|
| Built for | High-throughput, memory-efficient inference and serving | Serving and fine-tuning one model across mismatched machines you control |
| How a model is split | Tensor and pipeline parallelism across nodes. Every node needs an identical environment | Contiguous layer stages, sized to each card's memory and speed |
| Hardware | NVIDIA, AMD, Intel, and Apple through vLLM-Metal | Linux with NVIDIA GPUs |
| Fine-tuning | Not documented. It serves LoRA adapters, but does not train them | LoRA fine-tuning across the same split |
| API and interface | OpenAI-compatible, Anthropic Messages and gRPC. No bundled interface | OpenAI-compatible API and a web interface |
| Network | Your own trusted cluster on an isolated network | Workers connect out to your own coordinator: direct links where possible, its relay otherwise, across buildings and routers |
| Encryption between machines | None: its security docs call traffic between nodes insecure by default | None of its own between machines. A WireGuard mesh encrypts traffic when it is up |
| Licence | Apache-2.0, open source | Source not public, terms agreed during the evaluation |
| Status | Very active; stable release 2026-10-18 | In pre-evaluation |
Where vLLM is the better choice¶
- Raw throughput on modern cards, with continuous batching and prefix caching.
- Far more models, over 200 architectures, and more quantization formats.
- Maturity and community, with over 2,000 contributors.
- Open source, under Apache-2.0.
Where SilentSwarm fits¶
- No single card, or matching set of cards, can hold the model.
- The machines are mismatched, or behind different routers.
- You want to fine-tune across the same machines that serve.
SilentSwarm has gaps of its own: no sandbox, and no encryption of its own between machines or at rest. Data and security lists them, and our benchmarks show what it does on old cards.
Sources¶
SilentSwarm is in pre-evaluation. We are looking for companies, institutions and individuals who want to try it on their own machines: Apply for the evaluation.