genpark-kv-cache-paged-attention-allocator-skill

⚡ Overview & Architectural Significance

genpark-kv-cache-paged-attention-allocator-skill delivers zero-dependency, mathematically sound edge AI acceleration and inference optimization primitives engineered strictly using Python 3.9+ standard library.

🌟 Key Architectural Capabilities

• Zero External Dependencies: Operates exclusively via pure Python (math, random, time, json). Zero pip install overhead, zero CUDA/C++ compilation failures.

• Enterprise Edge Invariants: Implements formal INT8 symmetric quantization scales, non-contiguous PagedAttention virtual block mapping, speculative decoding rejection sampling, Radix trie prompt prefix caching, and high-precision TTFT/TPOT latency telemetry.

• Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.

Related MCP server: genpark-agent-hierarchical-episodic-memory-skill

🏗️ Architectural Topology & Pipeline

flowchart TD
    PromptStream["Prompt & Token Input Stream"] --> PrefixCache["Radix Dynamic Prefix Cache"]
    PrefixCache -->|Cache Miss| PrefillStage["Prefill / KV-Cache Paged Allocation"]
    PrefixCache -->|Cache Hit| KVReuse["Zero-Compute KV-Cache Reuse"]
    KVReuse --> DecodingLoop["Speculative Decoding Loop"]
    PrefillStage --> PagedAlloc["PagedAttention Block Allocator"]
    PagedAlloc --> DecodingLoop
    DecodingLoop --> DraftVerify["Speculative Draft Verification Engine"]
    DraftVerify --> QuantKernel["Int8 Symmetric Quantized GEMM"]
    QuantKernel --> Telemetry["Edge Inference Latency & Jitter Telemetry"]

🚀 Quickstart & Standalone Execution

Local Python Client Usage

from client import PagedKVCacheAllocator

# Initialize engine
engine = PagedKVCacheAllocator()

# Execute self-testing benchmark suite
result = engine.benchmark_allocation()
print("Execution Result:", result)

🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your claude_desktop_config.json or cursor.json:

{
  "mcpServers": {
    "genpark-kv-cache-paged-attention-allocator-skill": {
      "command": "python",
      "args": ["-u", "/path/to/genpark-kv-cache-paged-attention-allocator-skill/mcp_server.py"]
    }
  }
}

📦 Smithery.ai & PyPI Deployment

This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:

pip install git+https://github.com/alphaparkinc/genpark-kv-cache-paged-attention-allocator-skill.git