---
title: "vLLM"
description: "vLLM API guide - OpenAI-compatible self-hosted inference, chat, text, embeddings, rerank, and streaming"
icon: "v"
---

## Overview

vLLM is an **OpenAI-compatible provider** for self-hosted inference. Bifrost delegates to the shared OpenAI provider implementation. Key characteristics:
- **OpenAI compatibility** - Chat, text completions, embeddings, rerank, and streaming
- **Self-hosted** - Typically runs at `http://localhost:8000` or your own server
- **Optional authentication** - API key often omitted for local instances
- **Responses API** - Supported via chat completion fallback

### Supported Operations

| Operation | Non-Streaming | Streaming | Endpoint |
|-----------|---------------|-----------|----------|
| Chat Completions | ✅ | ✅ | `/v1/chat/completions` |
| Responses API | ✅ | ✅ | `/v1/chat/completions` |
| Text Completions | ✅ | ✅ | `/v1/completions` |
| Embeddings | ✅ | - | `/v1/embeddings` |
| Rerank | ✅ | - | `/v1/rerank` (fallback: `/rerank`) |
| List Models | ✅ | - | `/v1/models` |
| Image Generation | ❌ | ❌ | - |
| Speech (TTS) | ❌ | ❌ | - |
| Transcriptions (STT) | ✅ | ✅ | `/v1/audio/transcriptions` |
| Files | ❌ | ❌ | - |
| Batch | ❌ | ❌ | - |

<Note>
**Unsupported Operations** (❌): Image Generation, Speech, Files, and Batch are not supported and return `UnsupportedOperationError`.
</Note>

---

## Authentication

- **API key**: Optional. For local vLLM instances, the key is often left empty.
- When set, the key is sent as `Authorization: Bearer <key>`.

---

## Configuration

- **Base URL**: Default is `http://localhost:8000`. Override via provider `network_config.base_url`.
- **Model names**: Depend on the models loaded in your vLLM instance (e.g. `meta-llama/Llama-3.2-1B-Instruct`, `BAAI/bge-m3` for embeddings).

<Tabs>
<Tab title="Gateway">

```bash
# Point to local or remote vLLM instance (default: http://localhost:8000)
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vllm/meta-llama/Llama-3.2-1B-Instruct",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# Gateway provider config: set base_url for remote vLLM
# "network_config": { "base_url": "http://vllm-endpoint:8000" }
```

</Tab>
<Tab title="Go SDK">

```go
config := &schemas.ProviderConfig{
    NetworkConfig: schemas.NetworkConfig{
        BaseURL: "http://localhost:8000",  // optional; default is http://localhost:8000
        DefaultRequestTimeoutInSeconds: 30,
    },
}
provider, _ := vllm.NewVLLMProvider(config, logger)

response, _ := provider.ChatCompletion(ctx, key, request)
```

</Tab>
</Tabs>

---

## Getting started

1. Run a vLLM server (Docker or pip). Example with Docker:
   ```bash
   docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest --model meta-llama/Llama-3.2-1B-Instruct
   ```
2. Verify the server:
   ```bash
   curl http://localhost:8000/v1/models
   ```
3. Use Bifrost with model prefix `vllm/<model_id>` (e.g. `vllm/meta-llama/Llama-3.2-1B-Instruct`).

---

# 1. Chat Completions

vLLM supports standard OpenAI chat completion parameters. For full parameter reference, see [OpenAI Chat Completions](/providers/supported-providers/openai#1-chat-completions). Message types, tools, and streaming follow the same behavior.

---

# 2. Responses API

Bifrost converts Responses API requests to Chat Completions and back:

```
BifrostResponsesRequest
  → ToChatRequest()
  → ChatCompletion
  → ToBifrostResponsesResponse()
```

---

# 3. Text Completions

| Parameter | Mapping |
|-----------|---------|
| `prompt` | Sent as-is |
| `max_tokens` | max_tokens |
| `temperature` | temperature |
| `top_p` | top_p |
| `stop` | stop sequences |

---

# 4. Embeddings

vLLM supports `/v1/embeddings`. Use model IDs exposed by your vLLM server (e.g. `BAAI/bge-m3`).

---

# 5. List Models

Lists models from your vLLM instance via `/v1/models`. Available models depend on what is loaded on the server.

---

# 6. Rerank

vLLM supports reranking for pooling/cross-encoder reranker models. Bifrost sends requests to `/v1/rerank` and automatically falls back to `/rerank` when required by your vLLM deployment.

```bash
curl -X POST http://localhost:8080/v1/rerank \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vllm/BAAI/bge-reranker-v2-m3",
    "query": "What is machine learning?",
    "documents": [
      {"text": "Machine learning is a subset of AI."},
      {"text": "Python is a programming language."},
      {"text": "Deep learning uses neural networks."}
    ],
    "params": {
      "return_documents": true
    }
  }'
```

<Note>
Your upstream vLLM server must be started with a rerank-capable model (pooling/cross-encoder task support).
</Note>

---

## Caveats

<Accordion title="Default base URL is localhost">
**Severity**: Low  
**Behavior**: Default base URL is `http://localhost:8000`.  
**Impact**: For remote or custom ports, set `network_config.base_url` in the provider config.  
</Accordion>

<Accordion title="Error responses with HTTP 200">
**Severity**: Low  
**Behavior**: vLLM may return HTTP 200 with an error payload (e.g. `{"error": {"code": 404, "message": "..."}}`) instead of 4xx/5xx.  
**Impact**: Bifrost normalizes these into standard error responses so clients see consistent error handling.  
</Accordion>