mlsanityData to 7 Oct 2026

BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration

arXiv 2610.02800 ↗First posted 2 Oct 2026cs.AIcited by 0

On the map: Efficient LLM inference and serving → Speculative decoding

Abstract (arXiv)

Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, existing methods often require an additional draft model or weight representation, introducing non-negligible memory overhead on resource-constrained devices. Self-speculative approaches reduce this overhead, yet still face trade-offs between draft quality, target quality, and storage efficiency. We propose BitNest, a bit-nested speculative decoding framework that embeds a low-precision draft directly into the higher-precision target representation. Instead of deriving a draft from a predefined target, BitNest first constructs a strong low-precision base and then recovers the higher-precision target through residual refinement, enabling both models to share a single physical weight representation. BitNest further extends this progressive-precision design to the KV cache for long-context inference. Across multiple 7B--8B edge-friendly LLMs and diverse workloads, BitNest achieves an average speculative acceptance rate of 95.2% while closely preserving higher-precision model quality, and delivers 1.48--1.61x end-to-end speedup over FP16 autoregressive decoding. On the LLaMA models supported by all representative self-speculative baselines, BitNest also achieves consistently competitive or higher decoding speedup.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving. Builds on 30 of its 36 references that are in the map. First posted on arXiv 2 Oct 2026.

Builds on

30 of 36 references.

Closest ancestors · 13

Same direction, newest first

  1. 2026DFlash: Block Diffusion for Flash Speculative Decoding5 Feb
  2. 2025SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding1 Nov
  3. 2025DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding28 Sep
  4. 2025EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test3 Mar
  5. 2025QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache5 Feb
  6. 2024QSpec: Speculative Decoding with Complementary Quantization Schemes15 Oct
  7. 2024SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration9 Oct
  8. 2024EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees24 Jun
  9. 2024Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting29 Apr
  10. 2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty26 Jan
  11. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads19 Jan
  12. 2023Accelerating Large Language Model Decoding with Speculative Sampling2 Feb
1 more
  1. 2022Fast Inference from Transformers via Speculative Decoding30 Nov

Other references · 17

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2021Program Synthesis with Large Language Modelscited by 3,509
  4. 2016Pointer Sentinel Mixture Modelscited by 2,992
  5. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,838
  6. 2025π0.5: a Vision-Language-Action Model with Open-World Generalizationcited by 1,653
  7. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
  8. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,338
  9. 2025Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Successcited by 844
  10. 2019Compressive Transformers for Long-Range Sequence Modellingcited by 699
  11. 2024QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMscited by 598
  12. 2024SpinQuant: LLM quantization with learned rotationscited by 446
5 more
  1. 2024QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebookscited by 354
  2. 2021Efficient Attentions for Long Document Summarizationcited by 342
  3. 2024LayerSkip: Enabling Early Exit Inference and Self-Speculative Decodingcited by 302
  4. 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMscited by 165
  5. 2025Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potentialcited by 46

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference
  2. 2025Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference
  3. 2025Tutorial Proposal: Speculative Decoding for Efficient LLM Inference
  4. 2025Yggdrasil: Bridging Dynamic Speculation and Static Runtime for Latency-Optimal Tree-Based LLM Decoding
  5. 2023Accelerating LLM Inference with Staged Speculative Decoding
  6. 2025Scaling LLM Speculative Decoding: Non-Autoregressive Forecasting in Large-Batch Scenarios
  7. 2026FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration
  8. 2026CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration