Target Model
The Target Model is the primary, high-capacity Large Language Model (LLM) responsible for generating final outputs in inference pipelines. In standard autoregressive generation, the Target Model computes probabilities for the next token sequentially, which is computationally expensive. In accelerated inference architectures, particularly those utilizing speculative-decoding, the Target Model serves as the authoritative verifier for tokens proposed by a smaller, faster draft-model.
Role in Speculative Decoding
In speculative decoding frameworks, the Target Model’s role shifts from pure generation to verification:
- Verification: The Target Model evaluates tokens generated by the Draft Model in parallel. If the Target Model agrees with the Draft Model’s prediction, the token is accepted, saving computation.
- Correction: If the Target Model disagrees, it rejects the proposed token and generates the correct token itself, resetting the speculative process.
- Accuracy Preservation: The Target Model ensures that the output distribution remains identical to that of standard autoregressive decoding, maintaining quality while improving throughput.
Recent Developments
- DeepSeek DSpark Integration: Recent advancements highlight the use of the Target Model in conjunction with DeepSeek’s DSpark acceleration layer. DeepSeek DSpark: LLM Inference Acceleration via Enhanced Speculative Decoding demonstrates how enhanced speculative decoding techniques can double inference speed for models like Qwen3 by optimizing the interaction between the draft and target stages.