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Inference

Parallel TTC

2024ActivePublished: 3 May 2026Updated: 3 May 2026Published
Key innovation
Generates multiple candidate responses in parallel at inference time and selects the best output, trading compute for accuracy without changing model weights.
Category
Inference
Abstraction level
Paradigm
Operation level
Inference
Use cases
Hard reasoning tasksScientific research assistanceComplex code generationMathematical proof verification

How it works

The model is run N times in parallel (or sequentially with different seeds/temperatures). Outputs are evaluated by an external scorer (reward model, verifier, majority voting, or best-of-N heuristic). The highest-scoring response is returned to the user.

Problem solved

Standard next-token sampling produces a single response of limited quality. Parallel TTC trades additional time and compute cost for higher accuracy.

Implementation

Implementation pitfalls
Aggregating results from parallel paths is non-trivialMedium

Majority voting works for categorical answers, but for open-ended generation (essays, code) there is no simple aggregation method. Best-of-N requires a strong verifier/reward.

Inference costs grow linearly with the number of pathsMedium

N parallel samples = N× inference cost. At high N the quality gain saturates while cost grows — the optimal cost/quality ratio point must be empirically determined.

Execution paradigm

Primary mode
Conditional

The number of candidates (N) can be fixed or scale adaptively with query difficulty.

Activation pattern
Input dependent

Parallelism

Parallelism level
Fully parallel

Each candidate can be generated independently on a separate GPU/TPU.

Scope
Inference

Hardware requirements

Primary

Each candidate can be generated on a dedicated GPU; NVLink-based architectures (e.g., NVIDIA GB200 NVL72) enable efficient parallelization.