Laguna XS.2 Inference Optimization with vLLM, Speculators, and LLM Compressor

Poolside and Red Hat AI have optimized the Laguna XS.2 model for high-performance agentic coding and long-horizon software tasks. By integrating Laguna XS.2 into vLLM and implementing DFlash speculative decoding and LLM Compressor quantization, the team has reduced inference latency and increased operational efficiency without sacrificing generation quality.

First-Class vLLM Integration

Laguna XS.2 is integrated directly into vLLM as a first-class citizen. This integration allows developers to deploy the model immediately using standard vLLM APIs, ensuring seamless compatibility with existing production inference pipelines.

Accelerating Inference with DFlash Speculative Decoding

Red Hat AI utilized the Speculators library to train a DFlash speculator for Laguna XS.2, resulting in a 2-3x increase in token generation speed with no loss in generation quality.

Technical Implementation of DFlash

The DFlash algorithm enables a small 5-layer, 0.6B draft model to predict a block of tokens using hidden state inputs from the target Laguna XS.2 model. These predicted tokens are then verified by Laguna XS.2 in a single forward pass. If the tokens are accepted, they are produced significantly faster than standard autoregressive generation. This verification process guarantees that the output quality remains identical to using the large model alone.

Training Methodology

The DFlash speculator was trained using the following parameters:

• Datasets: 500k samples from Ultrachat 200k SFT and Magpie-Align. Prompts were sampled and responses were regenerated from Laguna XS.2 with thinking enabled.
• Training Duration: 6 epochs using a cosine scheduler.
• Learning Rate: Maximum learning rate of 6e-4.
• Sequence Length: 8192.
• Sampling: 3072 block positions were randomly sampled for each sequence.

This configuration allows the 5-layer drafter to predict 8 tokens in a single forward pass, reducing inter-token latency by moving beyond the Eagle-3 paradigm to provide faster, parallel drafting.

Efficient Deployment via LLM Compressor Quantization

To support diverse hardware and memory requirements, Poolside released quantized checkpoints of Laguna XS.2 using the LLM Compressor library in the compressed-tensors format. These available variants include:

• FP8
• NVFP4
• INT4/INT8

These quantized checkpoints enable developers to optimize the balance between hardware utilization, latency, and memory consumption while maintaining model quality within vLLM.