Slot Based Concurrent Request Management With Kv Cache Allocation

via “slot-based concurrent request management with kv cache allocation”

Single-file executable LLMs — bundle model + inference, runs on any OS with zero install.

Unique: Allocates separate KV cache slots per concurrent request, enabling true parallel inference without cache collisions, versus naive approaches that serialize requests or risk cache corruption

vs others: Higher throughput than single-threaded inference because multiple requests process in parallel with independent cache slots, versus alternatives that queue requests sequentially

via “pagedattention-based kv cache memory management”

High-throughput LLM serving engine — PagedAttention, continuous batching, OpenAI-compatible API.

Unique: Introduces block-level virtual memory paging for KV caches (inspired by OS page tables) rather than request-level allocation, enabling fine-grained reuse and prefix sharing across requests without memory fragmentation

vs others: Achieves 10-24x higher throughput than HuggingFace Transformers' contiguous KV allocation by eliminating memory waste from padding and enabling aggressive request batching

via “paged kv cache management with disaggregated serving support”

NVIDIA's LLM inference optimizer — quantization, kernel fusion, maximum GPU performance.

Unique: Implements a block-based paging system (similar to OS virtual memory) where KV cache is divided into fixed-size blocks that can be allocated, freed, and reused across requests. Integrates with PyExecutor's event loop to track block lifecycle and enable zero-copy transfers between prefill and decode workers via shared GPU memory.

vs others: More memory-efficient than vLLM's paged attention (which uses a simpler allocation strategy) and supports disaggregated serving architectures that vLLM doesn't natively support, enabling 2-3x higher throughput on prefill-heavy workloads.

via “multi-tier kv cache storage with hicache and storage backends”

Fast LLM/VLM serving — RadixAttention, prefix caching, structured output, automatic parallelism.

Unique: Implements a three-tier storage hierarchy (GPU VRAM → CPU RAM → NVMe) with predictive migration logic that monitors access patterns and proactively moves data between tiers. Includes configurable storage backends and transfer optimization for each tier boundary.

vs others: Enables serving sequences 2-4x longer than vLLM on the same hardware by intelligently spilling to CPU/NVMe, with prefetching logic that hides transfer latency for predictable access patterns.

via “request-scheduling-and-concurrent-model-execution”

Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

Unique: Scheduler integrates with KV cache system to share cached context across requests for the same model, reducing memory overhead when processing similar prompts. Runner management is transparent — users don't configure runners; the scheduler auto-allocates based on available VRAM.

vs others: Simpler than vLLM's scheduler because it doesn't require explicit batching configuration; more memory-efficient than naive sequential processing because KV cache is shared across requests

via “kv cache management with automatic eviction and reuse”

Optimized quantized LLM inference for consumer GPUs — EXL2/GPTQ, flash attention, memory-efficient.

Unique: Implements automatic KV cache allocation and eviction with prefix-based reuse, where identical prompt prefixes share the same cache entries. This reduces memory overhead for multi-turn conversations and batch processing with shared prompts.

vs others: More memory-efficient than naive KV cache management because it reuses cache for identical prefixes and automatically evicts old entries, whereas naive approaches allocate fixed cache space upfront and cannot adapt to variable sequence lengths.

via “paged kv cache management with prefix sharing”

OpenAI and Anthropic compatible server for Apple Silicon. Run LLMs and vision-language models (Llama, Qwen-VL, LLaVA) with continuous batching, MCP tool calling, and multimodal support. Native MLX backend, 400+ tok/s. Works with Claude Code.

Unique: Adapts vLLM's paged KV cache design to MLX's unified memory architecture, enabling efficient cache sharing across requests while respecting Apple Silicon's memory constraints; tracks page allocation state to prevent fragmentation

vs others: More memory-efficient than contiguous caching for multi-request scenarios; enables longer context windows than naive caching; better cache utilization than request-level caching

via “multi-level kv cache management with prefix caching”

A high-throughput and memory-efficient inference and serving engine for LLMs

Unique: Implements block-level KV cache with prefix caching that tracks cache blocks as first-class objects with ownership and eviction policies, enabling cache reuse across requests without recomputation. Supports disaggregated serving via KV cache transfer protocol, allowing cache to be stored on dedicated cache servers separate from compute workers.

vs others: Reduces memory usage by 20-40% on multi-turn conversations vs. standard KV cache by reusing cached prefixes; disaggregated serving enables 10x larger batch sizes by decoupling cache capacity from compute capacity.

via “pagedattention-based kv cache management with memory pooling”

A high-throughput and memory-efficient inference and serving engine for LLMs

Unique: Pioneered paging-based KV cache management (PagedAttention) with block-level granularity and LRU eviction, enabling 4-8x higher batch sizes than contiguous allocation; most alternatives use simple contiguous buffers or naive reallocation strategies

vs others: Achieves 2-4x memory efficiency vs. TensorRT-LLM's contiguous cache and 3-5x vs. Hugging Face Transformers' naive approach, enabling production-scale batching on consumer GPUs