12 papers · 1 filter
LLM-42: Enabling Determinism in LLM Inference with Verified Speculation
Raja Gond, Aditya K Kamath, Ramachandran Ramjee +1
In LLM inference, the same prompt may yield different outputs across different runs. At the system level, this non-determinism arises from floating-point non-associativity combined…
Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference
Dhruv Deshmukh, Saurabh Goyal, Nipun Kwatra +1
Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG. We propose Kascade, a training-free s…
No Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha
Amey Agrawal, Haoran Qiu, Junda Chen +6
Deploying million-token Large Language Models (LLMs) is challenging because production workloads are highly heterogeneous, mixing short queries and long documents. This heterogenei…
On Evaluating Performance of LLM Inference Serving Systems
Amey Agrawal, Nitin Kedia, Anmol Agarwal +5
The rapid evolution of Large Language Model (LLM) inference systems has yielded significant efficiency improvements. However, our systematic analysis reveals that current evaluatio…
Niyama : Breaking the Silos of LLM Inference Serving
Kanishk Goel, Jayashree Mohan, Nipun Kwatra +2
The widespread adoption of Large Language Models (LLMs) has enabled diverse applications with very different latency requirements. Existing LLM serving frameworks rely on siloed in…
POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference
Aditya K Kamath, Ramya Prabhu, Jayashree Mohan +3
Each request in LLM inference goes through two phases: compute-bound prefill and memory-bandwidth-bound decode. To improve GPU utilization, recent systems use hybrid batching that…