PRISM: Parametrically Refactoring Inference for Speculative Sampling Draft Models
PRISM: Parametrically Refactoring Inference for Speculative Sampling Draft Models explores PRISM refactors computational pathways of draft models for faster LLM decoding, boosting throughput by over 2.6x.. Commercial viability score: 5/10 in LLM Optimization.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisTIDE: Temporal Incremental Draft Engine for Self-Improving LLM InferenceViability: 3.0
- Builds On ThisPRISM: Pushing the Frontier of Deep Think via Process Reward Model-Guided InferenceViability: 4.0
- Higher ViabilitySDFP: Speculative Decoding with FIT-Pruned Models for Training-Free and Plug-and-Play LLM AccelerationViability: 7.0
- Higher ViabilityEfficiently Aligning Draft Models via Parameter- and Data-Efficient AdaptationViability: 8.0
- Higher ViabilityWhen Drafts Evolve: Speculative Decoding Meets Online Learning
Related Resources
- How can LLM optimization be used to improve the efficiency of LLM fine-tuning?(question)
- How do frameworks like OptiKIT democratize LLM optimization for non-expert teams?(question)
- How can LLM optimization techniques contribute to more sustainable AI practices by reducing energy consumption?(question)