The Token Regeneration Bottleneck

Anyone who’s watched an LLM laboriously regenerate entire code blocks to insert a single line change understands the frustration of sequential token generation. This inefficiency stems from a fundamental constraint: while input tokens process in parallel (thousands per second), output tokens generate sequentially at ~100x slower speeds. Cascade Technologies now challenges this paradigm with its novel Predicted Outputs implementation for VLLM, promising dramatic speedups by leveraging known text predictions.

How Predicted Outputs Rewrites the Rules

The technique injects a user-provided prediction (e.g., existing code for an agent to modify) into the decoding pipeline. When the LLM’s output aligns with this prediction, tokens skip sequential generation and process in parallel—like input tokens. Divergences trigger realignment via CPU-based diff algorithms (Myers diff) with near-zero latency overhead. Crucially:

  • Accuracy Preservation: Only exact matches are accepted; output quality remains unchanged
  • Linear Scaling: 50% prediction accuracy ≈ 50% faster generation; 100% accuracy → near-instant
  • No GPU Overhead: Alignment runs on CPU, hiding latency behind generation cycles

“Tokens in green are generated nearly ‘for free,’” explains Cascade’s benchmark, showing a multiplayer game code modification completing 2.6x faster despite just 26-40% prediction acceptance.

Beyond OpenAI’s Flawed Implementation

While OpenAI’s API supports a similar feature, Cascade notes it often slows down generation due to suboptimal alignment handling. Their VLLM integration instead uses speculative decoding mechanics—replacing the draft model with static text—yielding near-linear efficiency gains. The implementation hooks into OpenAI’s prediction parameter, making adoption seamless:

# Standard OpenAI request with prediction
response = openai.Completion.create(
  model="vllm-engine",
  prompt="Update this code for multiplayer",
  prediction=original_code  # Cascade’s magic accelerator
)

Use Cases: Where Predictions Shine

  • Coding Agents: Modifying large files by predicting original code + changes
  • Structured Outputs: Using schema examples as predictions for JSON/XML generation
  • Document Editing: Updating reports or wikis where prior versions inform new drafts
  • Agent State Updates: Accelerating repetitive memory dict generation

The Road to Instantaneous Generation

Cascade’s approach transforms predictions from a niche trick into a scalable accelerator. As LLMs increasingly handle iterative tasks (like code refinement), avoiding redundant regeneration becomes critical. The open-source VLLM fork invites developers to test-drive the future—where knowing part of your output no longer means waiting for it.

Source: Cascade Technologies Blog