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genpark-dynamic-prompt-prefix-cache-skill

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by alphaparkinc
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Python
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    • genpark-dynamic-prompt-prefix

      Alpha-Park
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables inference engines to match longest common prompt token prefixes via a radix trie, so cached KV-cache blocks are reused instead of re-prefilled. This eliminates redundant prefill computation and lowers time-to-first-token, alongside paged attention allocation, speculative decoding verification, INT8 quantization, and latency telemetry.
      7
      MIT

    • genpark-speculative-decoding

      Alpha-Park
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables verification of draft-target speculative decoding with rejection sampling, acceptance-rate telemetry, and dynamic speedup estimation, alongside edge inference primitives such as INT8 quantization, paged KV allocation, and radix prefix caching.
      8
      MIT
    • genpark-kv-cache-paged-attention-allocator-skill by alphaparkinc

      genpark-kv-cache-pagedofficial

      alphaparkinc
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP clients to manage LLM inference memory through virtual paged-attention KV-cache block mapping, non-contiguous physical page allocation, zero-copy fragmentation tracking, radix-trie prefix caching, INT8 quantized compute, and speculative decoding verification. It also exposes prefill and decode latency telemetry so edge deployments can be benchmarked and tuned without external dependencies.
      7
      MIT

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