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.
Enables LLM agents to route responses as accept, verify, or ask-a-human based on token logprobs, and provides an MCP server for delegating generation to local models with confidence bands.
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.
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.
Provides an MCP-compatible profiler for measuring edge and on-device LLM inference latency, including TTFT, TPOT, tokens-per-second throughput, and P50/P90/P99 jitter distributions. It enables agents and developers to run these telemetry benchmarks from MCP clients such as Claude Desktop, Cursor, and Windsurf.