Hardware-Aware Efficient Inference for Long-Context and Reasoning Language Models: Low-Bit Quantization, KV-Cache Compression, and Speculative Decoding

Abstract

The deployment cost of large language models increasingly depends not only on parameter count but also on context length, reasoning-token generation, key-value cache growth, accelerator memory bandwidth, batch size, and decoding strategy. These pressures are amplified in reasoning models, which may generate thousands of intermediate tokens, and in long-context systems, where persistent key-value states can dominate runtime memory consumption. This review examines hardware-aware language-model inference techniques through September 2026. Post-training quantization methods including GPTQ, SmoothQuant, AWQ, and low-bit weight formats substantially reduced parameter-memory requirements. BitNet investigated extremely low-bit model representations trained natively rather than quantized after training. FlashAttention and FlashAttention-2 improved exact attention by restructuring memory access, while vLLM's PagedAttention improved key-value-cache management for concurrent serving. StreamingLLM and related long-context methods highlighted the importance of cache organization for persistent generation. KIVI and subsequent KV-cache quantization methods targeted the rapidly growing inference-state memory associated with long contexts. Speculative decoding reduced latency by allowing smaller draft models to propose tokens verified by a larger target model, while Medusa and EAGLE expanded multi-token prediction and draft-generation strategies. By 2026, hardware-aware inference increasingly involved adaptive precision and sensitivity-aware cache compression. ChanMix introduced mixed-precision allocation across key-value-cache channels for long-context inference. AnyBCQ proposed flexible hardware-oriented binary-coded quantization enabling runtime multi-precision operation. Hierarchical Block Quantization explored block-level hardware efficiency alongside second-level scaling, while asynchronous test-time scaling demonstrated that reasoning throughput can also be improved through speculative and asynchronous execution. The review argues that model quality and inference efficiency must increasingly be co-designed across algorithms, numeric formats, kernels, cache architectures, and serving systems.