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LLM inference optimization is crucial for enhancing the performance and efficiency of large language models in real-world applications. Recent advancements focus on strategies like adaptive KV cache loading, speculative decoding improvements, and dynamic quantization techniques to reduce latency and energy consumption. For instance, frameworks like SparKV and TIDE enable faster token processing by optimizing resource allocation and early exit strategies, while methods such as AQPIM and Alloc-MoE address memory constraints through innovative quantization and activation budget management. These developments are vital for builders aiming to deploy LLMs effectively, ensuring they can meet the demands of diverse applications while minimizing operational costs.
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Processing-in-Memory (PIM) architectures offer a promising solution to the memory bottlenecks in data-intensive machine learning, yet often overlook the growing challenge of activation memory footprin...
An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existing selection methods...
We introduce XFP, a dynamic weight quantizer for LLM inference that inverts the conventional workflow: the operator specifies reconstruction quality floors on per-channel cosine similarity (one strict...
Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offers a cost-effective way to adjust model behavior wi...
Speculative decoding accelerates LLM inference by drafting future tokens with a small model, but drafter models degrade sharply under template perturbation and long-context inputs. We identify a previ...
Efficient inference for on-device Large Language Models (LLMs) remains challenging due to limited hardware resources and the high cost of the prefill stage, which processes the full input context to c...
Large language models run every token through every layer, regardless of difficulty. We present TIDE, a post-training system that attaches tiny learned routers at periodic checkpoint layers and, at in...
Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. While NF4 (4-bit NormalFloat) quantization ena...
Large language models (LLMs) support long-context inference but suffer from substantial memory and runtime overhead due to Key-Value (KV) Cache growth. Existing KV Cache eviction methods primarily rel...
Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel. In practice, its speedup is often bottlenecked by a rigid verifi...
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Canonical ID llm-inference-optimization | Route /topic/llm-inference-optimization
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