Skip to main content
Glama
genpark-kv-cache-paged-attention-allocator-skill

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

Official

Related Servers

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

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP clients to coordinate local AI model runs by monitoring RAM, system commit, and VRAM, queueing tasks across clients, performing admission checks before inference, renewing leases, completing and releasing runs, and reporting status, history, and health while preserving original generation quality parameters. It also supports controlled start/stop and optional idle application memory reclamation.
      MIT
    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables profiling and optimizing edge and on-device LLM inference by measuring TTFT, TPOT, tokens/second throughput, and P50/P90/P99 jitter while providing standard-library primitives for quantization, paged attention, speculative decoding, and prefix caching.
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables zero-dependency INT8 symmetric quantization of LLM weights and activations on a per-tensor or per-channel basis, reporting SNR and MSE reconstruction telemetry along with TTFT/TPOT latency and jitter metrics. Also exposes edge inference primitives such as PagedAttention block allocation, radix prefix caching, and speculative decoding verification through the Model Context Protocol.
      7
      MIT
    • A
      license
      Not graded
      quality
      Not graded
      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
    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables LLM clients to manage hierarchical working, dialogue, and episodic memories with decay-aware consolidation, hybrid lexical/vector retrieval, knowledge-graph expansion, and semantic caching over MCP.
      7
      MIT