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https://aibrix.github.io/posts/2025-02-05-v0.2.0-release

AIBrix v0.2.0 Release: Distributed KV Cache, Orchestration and Heterogeneous GPU Support

We’re excited to announce the v0.2.0 release of AIBrix! Building on feedback from v0.1.0 production adoption and user interest, this release introduces several new features to enhance performance and usability. Extend the vLLM Prefix Cache with external distributed Dram based KV Cache pool Mix-Grain Multi-Node Inference Orchestration Cost efficient and SLO-driven Heterogenous Serving Accelerator Diagnostic and Failure Mockup Tools What’s New? Distributed KV Cache Pool The rising demand for large language models has intensified the need for efficient memory management and caching to optimize inference performance and reduce costs. In multi-round use cases like chatbots and agent-based systems, overlapping token sequences lead to redundant computations during the prefill phase, wasting resources and limiting throughput.



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AIBrix v0.2.0 Release: Distributed KV Cache, Orchestration and Heterogeneous GPU Support

https://aibrix.github.io/posts/2025-02-05-v0.2.0-release

We’re excited to announce the v0.2.0 release of AIBrix! Building on feedback from v0.1.0 production adoption and user interest, this release introduces several new features to enhance performance and usability. Extend the vLLM Prefix Cache with external distributed Dram based KV Cache pool Mix-Grain Multi-Node Inference Orchestration Cost efficient and SLO-driven Heterogenous Serving Accelerator Diagnostic and Failure Mockup Tools What’s New? Distributed KV Cache Pool The rising demand for large language models has intensified the need for efficient memory management and caching to optimize inference performance and reduce costs. In multi-round use cases like chatbots and agent-based systems, overlapping token sequences lead to redundant computations during the prefill phase, wasting resources and limiting throughput.



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https://aibrix.github.io/posts/2025-02-05-v0.2.0-release

AIBrix v0.2.0 Release: Distributed KV Cache, Orchestration and Heterogeneous GPU Support

We’re excited to announce the v0.2.0 release of AIBrix! Building on feedback from v0.1.0 production adoption and user interest, this release introduces several new features to enhance performance and usability. Extend the vLLM Prefix Cache with external distributed Dram based KV Cache pool Mix-Grain Multi-Node Inference Orchestration Cost efficient and SLO-driven Heterogenous Serving Accelerator Diagnostic and Failure Mockup Tools What’s New? Distributed KV Cache Pool The rising demand for large language models has intensified the need for efficient memory management and caching to optimize inference performance and reduce costs. In multi-round use cases like chatbots and agent-based systems, overlapping token sequences lead to redundant computations during the prefill phase, wasting resources and limiting throughput.

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      We’re excited to announce the v0.2.0 release of AIBrix! Building on feedback from v0.1.0 production adoption and user interest, this release introduces several new features to enhance performance and usability. Extend the vLLM Prefix Cache with external distributed Dram based KV Cache pool Mix-Grain Multi-Node Inference Orchestration Cost efficient and SLO-driven Heterogenous Serving Accelerator Diagnostic and Failure Mockup Tools What’s New? Distributed KV Cache Pool The rising demand for large language models has intensified the need for efficient memory management and caching to optimize inference performance and reduce costs. In multi-round use cases like chatbots and agent-based systems, overlapping token sequences lead to redundant computations during the prefill phase, wasting resources and limiting throughput.
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      We’re excited to announce the v0.2.0 release of AIBrix! Building on feedback from v0.1.0 production adoption and user interest, this release introduces several new features to enhance performance and usability. Extend the vLLM Prefix Cache with external distributed Dram based KV Cache pool Mix-Grain Multi-Node Inference Orchestration Cost efficient and SLO-driven Heterogenous Serving Accelerator Diagnostic and Failure Mockup Tools What’s New? Distributed KV Cache Pool The rising demand for large language models has intensified the need for efficient memory management and caching to optimize inference performance and reduce costs. In multi-round use cases like chatbots and agent-based systems, overlapping token sequences lead to redundant computations during the prefill phase, wasting resources and limiting throughput.
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