Non-goals

Meridian has explicit scope boundaries. Documenting what it does not do is as important as documenting what it does — it prevents inflated expectations and makes the design auditable.

Not a serving engine

Meridian is a scheduling layer. It requires vLLM (or a compatible serving backend) underneath it. It does not implement:

• Model loading or weight management.
• Prefill execution (prompt processing).
• Decode kernel scheduling (CUDA streams, tensor parallelism).
• Tokenisation or detokenisation.
• HTTP/gRPC serving interfaces.

Not a throughput optimiser

Meridian optimises latency differentiation — protecting output-phase inter-token latency at the cost of slightly reduced think-phase throughput. It does not optimise:

• Raw tokens/sec/GPU throughput. For throughput, run vLLM's own scheduler.
• Batch filling efficiency in non-reasoning workloads.
• Speculative decoding, continuous batching variants, or chunked prefill.

Not an accuracy guarantee

Budget forcing injects </think> based on entropy signals, not correctness. This can shorten reasoning chains on hard problems. Meridian does not:

• Measure or guarantee reasoning accuracy.
• Validate that forced-short chains produce correct answers.
• Implement any feedback loop between output quality and forcing thresholds.

Operators running accuracy-sensitive workloads should validate that their eat_ema_variance_threshold and rpdi_threshold settings do not degrade task accuracy on representative prompts.

Not a multi-model router

Meridian schedules within a single vllm.AsyncLLMEngine instance. It does not:

• Route requests across multiple models.
• Implement A/B model experiments.
• Manage model replicas or horizontal scaling.

Not a vLLM fork

Meridian is a plugin, not a fork. It does not modify vLLM internals and does not ship a patched vLLM binary. Compatibility with a specific vLLM version is the operator's responsibility. See the Compatibility Matrix.

Not production-certified

v0.1.0 is an early release. It has not been validated under production traffic at scale. Known gaps:

• Real-vLLM end-to-end results exist only for Qwen/Qwen2.5-0.5B in the repo's GPU CI workflow.
• The disagg fabric integration uses a synthetic in-process mock when libnixl is not available; real NIXL interop has not been exercised in CI.
• No back-pressure mechanism exists if the disagg fabric becomes unavailable.