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nvidia/speculative-decoding/README.md
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@ -7,8 +7,8 @@
- [Overview](#overview)
- [Instructions](#instructions)
- [Step 1. Configure Docker permissions](#step-1-configure-docker-permissions)
- [Step 2. Run Draft-Target Speculative Decoding](#step-2-run-draft-target-speculative-decoding)
- [Step 3. Test the Draft-Target setup](#step-3-test-the-draft-target-setup)
- [Step 2. Run draft-target speculative decoding](#step-2-run-draft-target-speculative-decoding)
- [Step 3. Test the draft-target setup](#step-3-test-the-draft-target-setup)
- [Troubleshooting](#troubleshooting)
- [Cleanup](#cleanup)
- [Next Steps](#next-steps)
@ -39,10 +39,12 @@ These examples demonstrate how to accelerate large language model inference whil
- NVIDIA Spark device with sufficient GPU memory available
- Docker with GPU support enabled
```bash
docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi
```
- HuggingFace authentication configured (if needed for model downloads)
```bash
huggingface-cli login
```
@ -55,14 +57,10 @@ These examples demonstrate how to accelerate large language model inference whil
**Risks:** GPU memory exhaustion with large models, container registry access issues, network timeouts during downloads
**Rollback:** Stop Docker containers and optionally clean up downloaded model cache
**Rollback:** Stop Docker containers and optionally clean up downloaded model cache.
## Instructions
## Traditional Draft-Target Speculative Decoding
This example demonstrates traditional speculative decoding using a smaller draft model to accelerate a larger target model.
### Step 1. Configure Docker permissions
To easily manage containers without sudo, you must be in the `docker` group. If you choose to skip this step, you will need to run Docker commands with sudo.
@ -82,7 +80,7 @@ sudo usermod -aG docker $USER
> **Warning**: After running usermod, you must log out and log back in to start a new
> session with updated group permissions.
### Step 2. Run Draft-Target Speculative Decoding
### Step 2. Run draft-target speculative decoding
Execute the following command to set up and run traditional speculative decoding:
@ -118,7 +116,7 @@ EOF
"
```
### Step 3. Test the Draft-Target setup
### Step 3. Test the draft-target setup
Once the server is running, test it by making an API call from another terminal:
@ -134,7 +132,7 @@ curl -X POST http://localhost:8000/v1/completions \
}'
```
#### Key Features of Draft-Target:
#### Key features of draft-target:
- **Efficient resource usage**: 8B draft model accelerates 70B target model
- **Flexible configuration**: Adjustable draft token length for optimization
- **Memory efficient**: Uses FP4 quantized models for reduced memory footprint
@ -169,4 +167,4 @@ docker stop <container_id>
- Experiment with different `max_draft_len` values (1, 2, 3, 4, 8)
- Monitor token acceptance rates and throughput improvements
- Test with different prompt lengths and generation parameters
- Read more on Speculative Decoding [here](https://nvidia.github.io/TensorRT-LLM/advanced/speculative-decoding.html)
- Read more on Speculative Decoding [here](https://nvidia.github.io/TensorRT-LLM/advanced/speculative-decoding.html).