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Here are
73 public repositories
matching this topic...
LvLLM is a special NUMA extension of vllm that makes full use of CPU and memory resources, reduces GPU memory requirements, and features an efficient GPU parallel and NUMA parallel architecture, supporting hybrid inference for MOE large models.
Python Multi-Process Execution Pool: concurrent asynchronous execution pool with custom resource constraints (memory, timeouts, affinity, CPU cores and caching), load balancing and profiling capabilities of the external apps on NUMA architecture
RAM Coffers: Conditional Memory via NUMA-Distributed Weight Banking - O(1) lookup routing for LLM inference (Dec 16, 2025 - predates DeepSeek Engram by 27 days)
A community-oriented list of useful NUMA-related libraries, tools, and other resources
Multi-core Window-Based Stream Processing Engine
AltiVec/VSX optimized llama.cpp for IBM POWER8
NUMAPROF is a NUMA memory profliler based on Pintool to track your remote memory accesses.
Lsglang is a special extension of sglang that fully utilizes CPU and GPU computing resources with an efficient GPU parallel + NUMA parallel architecture, suitable for MOE model hybrid inference.
Rust bindings to Open MPI Portable Hardware Locality "hwloc" library, covering version 2.0 and above.
Data Plane Development Kit (DPDK) integration into OpenWrt
NumaMMA is a lightweight memory profiler for parallel applications
Go package providing information about the number of CPUs in the system
👁 numanji
Local-affinity first NUMA-aware allocator with optional fallback.
NUMA-aware multi-CPU multi-GPU data transfer benchmarks
Non-unix, custom-API hybrid OS kernel written in C++ which can be thought of as an emulated microkernel. The native API is almost fully asynchronous and the kernel is aimed at high-scaling, high-throughput-requiring multiprocessor workloads, with working support for SMP and NUMA already implemented. Join the IRC channel, #zbz-dev on freenode!
cgroups-based cpuset isolator and resource estimator modules for mesos
A repo to allow validation of performance results in the knor paper and provide a fast, scalable k-means implementation.
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