Mocker

The Mocker is a lightweight, high-fidelity simulation of an LLM inference engine, implemented entirely in Rust. It replicates the core scheduling, memory management, and timing behaviors of production engines without requiring a GPU, making it invaluable for testing Dynamo’s routing, KV cache events, disaggregated serving, and planner components.

Overview

The mocker simulates:

• Block-based KV cache management with LRU eviction
• Engine-specific continuous batching schedulers for vLLM and SGLang
• Prefix caching with hash-based block deduplication
• Chunked prefill for better batching efficiency
• Realistic timing models for prefill and decode phases
• Disaggregated serving (prefill/decode separation)
• KV event publishing for router integration
• Data parallelism (multiple DP ranks per engine)

Note: While the mocker uses vLLM as its primary reference implementation, these core components—block-based KV cache management, continuous batching schedulers, LRU evictors, and prefix caching—are fundamental to all modern LLM inference engines, including SGLang and TensorRT-LLM. The architectural patterns simulated here are engine-agnostic and apply broadly across the inference ecosystem.

Quick Start

Basic Usage

$
# Launch a single mocker worker
$
python -m dynamo.mocker --model-path Qwen/Qwen3-0.6B
$
$
# Launch with custom KV cache configuration
$
python -m dynamo.mocker \
>
    --model-path Qwen/Qwen3-0.6B \
>
    --num-gpu-blocks-override 8192 \
>
    --block-size 64 \
>
    --max-num-seqs 256
$
$
# Launch with timing speedup for faster testing
$
python -m dynamo.mocker \
>
    --model-path Qwen/Qwen3-0.6B \
>
    --speedup-ratio 10.0

Disaggregated Serving

$
# Launch prefill worker
$
python -m dynamo.mocker \
>
    --model-path Qwen/Qwen3-0.6B \
>
    --disaggregation-mode prefill \
>
    --bootstrap-ports 50100
$
$
# Launch decode worker (in another terminal)
$
python -m dynamo.mocker \
>
    --model-path Qwen/Qwen3-0.6B \
>
    --disaggregation-mode decode

Multiple Workers in One Process

$
# Launch 4 mocker workers sharing the same tokio runtime
$
python -m dynamo.mocker \
>
    --model-path Qwen/Qwen3-0.6B \
>
    --num-workers 4

CLI Arguments

Environment Variables

Note: For local scale tests and router benchmarks, prefer --num-workers over launching many separate mocker processes. All workers share one tokio runtime and thread pool, which is both lighter weight and closer to how the test harnesses exercise the mocker.

Trace Replay

The mocker supports replaying Mooncake-style traces through the dedicated replay CLI, which exposes offline|online, round_robin|kv_router, arrival_speedup_ratio, closed-loop concurrency admission, synthetic workload generation, and offline disaggregated prefill/decode replay directly:

The replay CLI defaults to --replay-mode offline and --router-mode round_robin. Aggregated replay uses --extra-engine-args. Offline disagg replay instead uses --prefill-engine-args plus --decode-engine-args, together with --num-prefill-workers and --num-decode-workers.

$
python -m dynamo.replay /path/to/mooncake_trace.jsonl \
>
    --num-workers 4 \
>
    --replay-mode offline \
>
    --router-mode kv_router \
>
    --arrival-speedup-ratio 5 \
>
    --trace-block-size 512 \
>
    --extra-engine-args '{"block_size":64}' \
>
    --router-config '{"router_queue_policy":"fcfs"}' \
>
    --report-json /tmp/replay-report.json

The same CLI also supports synthetic replay without a trace file:

$
python -m dynamo.replay \
>
    --input-tokens 5000 \
>
    --output-tokens 500 \
>
    --request-count 1000 \
>
    --arrival-interval-ms 1.0 \
>
    --num-workers 1 \
>
    --replay-mode offline \
>
    --replay-concurrency 100 \
>
    --extra-engine-args '{"block_size":512}' \
>
    --report-json /tmp/replay-report.json

Synthetic replay also supports workload-style generation for shared-prefix and multi-turn tests:

$
python -m dynamo.replay \
>
    --input-tokens 5000 \
>
    --output-tokens 500 \
>
    --request-count 200 \
>
    --turns-per-session 3 \
>
    --shared-prefix-ratio 0.5 \
>
    --num-prefix-groups 8 \
>
    --inter-turn-delay-ms 250 \
>
    --replay-mode offline \
>
    --replay-concurrency 32 \
>
    --extra-engine-args '{"block_size":512}' \
>
    --report-json /tmp/replay-report.json

For trace files, replay also understands multi-turn sessions when records share session_id. The first turn uses timestamp/created_time; later turns can use delay or delay_ms:

1
{"session_id":"session-a","timestamp":1000,"input_length":2048,"output_length":128,"hash_ids":[1,2,3,4]}
2
{"session_id":"session-a","delay":250,"input_length":2560,"output_length":128,"hash_ids":[1,2,3,4,5]}

For trace-file replay, --trace-block-size controls how many tokens each hash_id represents in the dataset, while engine block_size still controls the replay engine and router hashing. Public Mooncake/toolagent traces use --trace-block-size 512; engine block_size can still stay at 64 to match the live runtime configuration.

The standalone replay CLI prints an AIPerf-style summary table to stdout and writes the full replay report JSON to disk.

Timing semantics:

• trace mode honors first-turn timestamps and inter-turn delays
• concurrency mode ignores first-turn timestamps but still enforces inter-turn delays
• in concurrency mode, TTFT is measured from actual dispatch under the in-flight cap

For full usage, constraints, and benchmarking guidance, see Mocker Trace Replay.

Replay supports aggregated vllm and sglang engine configs. Internally replay uses canonical block_size; for sglang, sglang.page_size is still accepted as a compatibility alias as long as it matches block_size when both are provided.

Offline replay also supports disaggregated kv_router mode. In that mode:

• --prefill-engine-args must describe a prefill worker
• --decode-engine-args must describe a decode worker
• --router-mode must be kv_router
• only offline replay is supported

Example:

$
python -m dynamo.replay \
>
    --input-tokens 4096 \
>
    --output-tokens 256 \
>
    --request-count 100 \
>
    --replay-mode offline \
>
    --router-mode kv_router \
>
    --replay-concurrency 32 \
>
    --num-prefill-workers 2 \
>
    --num-decode-workers 6 \
>
    --prefill-engine-args '{"worker_type":"prefill","block_size":512}' \
>
    --decode-engine-args '{"worker_type":"decode","block_size":512}' \
>
    --router-config '{"router_queue_policy":"wspt"}' \
>
    --report-json /tmp/replay-report.json

Performance Modeling Setup

By default, the mocker uses hardcoded polynomial formulas to estimate prefill and decode timing. For more realistic simulations, pass --planner-profile-data with either:

• a mocker-format .npz file, or
• a profiler output directory

The mocker automatically accepts profiler-style results directories and converts them internally.

It also accepts older raw-data directories containing:

• prefill_raw_data.json
• decode_raw_data.json

$
python -m dynamo.mocker \
>
    --model-path nvidia/Llama-3.1-8B-Instruct-FP8 \
>
    --planner-profile-data components/src/dynamo/planner/tests/data/profiling_results/H200_TP1P_TP1D \
>
    --speedup-ratio 1.0

AIC Performance Model

To use the AIC SDK for latency prediction:

$
uv pip install '.[mocker]'
$
$
python -m dynamo.mocker \
>
    --model-path nvidia/Llama-3.1-8B-Instruct-FP8 \
>
    --engine-type vllm \
>
    --aic-perf-model \
>
    --aic-system h200_sxm

The AIC model automatically uses --model-path and --engine-type to select the appropriate performance data. Available systems include h200_sxm, h100_sxm, etc. (see AIC SDK documentation for the full list).

Important notes:

• AIC is opt-in. If you do not pass --aic-perf-model, python -m dynamo.mocker does not use AIC.
• python -m dynamo.replay has two separate AIC surfaces:
  • engine timing AIC through --extra-engine-args / staged engine JSON
  • router-side prefill-load AIC through top-level --aic-* flags plus router_prefill_load_model="aic" in --router-config
• The Python AIC session bridge is now shared with the live KV router path via the internal dynamo._internal.aic module. Mocker CLI behavior is unchanged; this just removes duplicate AIC session code.
• aiconfigurator must be able to load the requested performance database for the selected system/backend/version. If the SDK is installed but the backing systems data is missing or unreadable, mocker now fails fast at startup with a clear error instead of failing later on first request.
• In development environments, this may require pointing Python at a source checkout of aiconfigurator with real Git LFS payloads materialized in its systems/ directory.

This mocker AIC path is separate from the router-side prefill-load estimator. Live router, frontend, and replay all use router_prefill_load_model="aic" plus top-level --aic-* flags for oldest-prefill prompt-load decay. Replay still uses engine-args AIC separately when you want the mocked worker timing model itself to come from AIC.

For aggregated replay, engine timing AIC still comes from --extra-engine-args:

$
python -m dynamo.replay /path/to/trace.jsonl \
>
    --extra-engine-args '{"aic_backend":"vllm","aic_system":"h200_sxm","aic_model_path":"nvidia/Llama-3.1-8B-Instruct-FP8","aic_tp_size":1}'

For offline disagg replay, pass the staged engine configs instead:

$
python -m dynamo.replay /path/to/trace.jsonl \
>
    --replay-mode offline \
>
    --router-mode kv_router \
>
    --prefill-engine-args '{"worker_type":"prefill","aic_backend":"vllm","aic_system":"h200_sxm","aic_model_path":"nvidia/Llama-3.1-8B-Instruct-FP8","aic_tp_size":1,"block_size":512}' \
>
    --decode-engine-args '{"worker_type":"decode","aic_backend":"vllm","aic_system":"h200_sxm","aic_model_path":"nvidia/Llama-3.1-8B-Instruct-FP8","aic_tp_size":1,"block_size":512}' \
>
    --num-prefill-workers 2 \
>
    --num-decode-workers 6

The aic_backend field enables the AIC perf model and should match engine_type ("vllm" or "sglang"). The aic_model_path field is the equivalent of --model-path in dynamo.mocker.

Replay router-side AIC prompt-load modeling is configured separately with top-level flags:

$
python -m dynamo.replay /path/to/trace.jsonl \
>
    --replay-mode offline \
>
    --router-mode kv_router \
>
    --num-workers 4 \
>
    --trace-block-size 512 \
>
    --extra-engine-args '{"block_size":64}' \
>
    --router-config '{"router_track_prefill_tokens":true,"router_prefill_load_model":"aic"}' \
>
    --aic-backend vllm \
>
    --aic-system h200_sxm \
>
    --aic-model-path nvidia/Llama-3.1-8B-Instruct-FP8 \
>
    --aic-tp-size 1

For offline disagg replay, the same top-level --aic-* flags drive the prefill-stage router only; the decode-stage router keeps prompt tracking disabled.

Example --reasoning configuration:

$
python -m dynamo.mocker \
>
    --model-path Qwen/Qwen3-0.6B \
>
    --reasoning '{"start_thinking_token_id":123,"end_thinking_token_id":456,"thinking_ratio":0.6}'

The profile results directory should contain:

• selected_prefill_interpolation/raw_data.npz
• selected_decode_interpolation/raw_data.npz

To generate profile data for your own model and hardware, run the profiler and then point --planner-profile-data at the resulting output directory.

Event Transport and Router Testing

The default event path uses the local indexer / event-plane subscriber flow. The older durable KV-events mode is still available through --durable-kv-events, but it is deprecated and should not be the preferred setup for new tests.

For router and indexer experiments that need native wire-format event forwarding, the mocker also supports a ZMQ path:

• --event-plane zmq
• --zmq-kv-events-ports for per-worker PUB base ports
• --zmq-replay-ports for optional replay/gap-recovery ROUTER base ports

When set, each worker binds on its base port plus dp_rank, so the number of comma-separated base ports must match --num-workers.

Disaggregation Port Layout

--bootstrap-ports takes a comma-separated list of base ports, one per worker. In multi-worker mode, the number of listed ports must exactly match --num-workers.

Prefill workers listen on these ports and publish the bootstrap endpoint through discovery. Decode workers use the matching ports to rendezvous before decode begins.

Kubernetes Deployment

The mocker can be deployed through example DynamoGraphDeployment manifests for both aggregated and disaggregated setups:

$
kubectl apply -f examples/backends/mocker/deploy/agg.yaml
$
kubectl apply -f examples/backends/mocker/deploy/disagg.yaml

Architecture

The mocker is organized into several cooperating components that mirror the internal architecture of production LLM inference engines. The scheduler (vLLM-style and SGLang-style variants) and KV block manager live inside the engine core. Multi-engine behavior — KV transfer/offloading simulation, KV router simulation, planner simulation — is added by the replay harness on top of multiple engine cores; see Mocker Trace Replay for the component-level diagram and for offline replay internals under lib/mocker/src/replay/offline/.

Scheduler

The mocker now has two scheduler shapes rather than one generic queue model:

• vLLM mocker uses an upstream-style waiting + running scheduler. Each request tracks computed tokens, the scheduler spends one token budget across the running set first, and decode pressure triggers inline preemption of running requests.
• SGLang mocker uses a cache-aware waiting/running scheduler around a radix-style prefix cache. It batches prefill work with decode-state awareness and handles pressure primarily through decode retraction while preserving cached prefixes.

Both schedulers simulate continuous batching, prefix reuse, chunked prefill, memory pressure, and decode token emission while publishing metrics about current resource utilization.

When resources become constrained, the mocker simulates the engine’s real recovery path:

• vLLM-style decode preemption and recompute
• SGLang-style decode retraction plus prefix-preserving cache updates

KV Block Manager

The mocker’s KV block manager is now built on kvbm-logical::BlockManager<G1>, the same logical block manager the real Dynamo runtime uses. The mocker wraps it in lib/mocker/src/kv_manager/kvbm_backend.rs and translates its own MoveBlock protocol onto kvbm-logical’s RAII lifecycle (allocate → stage → register → drop).

Blocks still conceptually live in one of two pools:

• Active — blocks currently held by at least one sequence. Partial (still-filling) blocks are held as MutableBlock<G1>; full blocks are held as ImmutableBlock<G1> clones (the clone vec length is the mocker’s refcount, one per Use).
• Inactive — blocks no longer referenced by any sequence but kept for prefix-cache reuse. Handled entirely by kvbm-logical’s inactive pool; the mocker never tracks them manually.

The lifecycle is RAII: dropping the last ImmutableBlock clone transitions the block from active to inactive (kvbm-logical’s reset pool), with no explicit deref/evict bookkeeping on the mocker side. When a sequence completes or is preempted, the mocker simply drops its handles; kvbm-logical recovers the capacity.

Three Use outcomes are tracked for KV-event emission: ActiveHit (bump refcount on an already-pinned block), InactiveHit (reactivate via match_blocks(plh)), and NewStore (fresh allocation). Only NewStore emits a Stored KV event — the router radix tree already knows about the other two and only forgets on explicit Removed.

Eviction Backends

The kvbm-logical inactive pool selects eviction victims via one of three backends, exposed as MockerEvictionBackend in lib/mocker/src/common/protocols.rs:

• Lineage (default) — parent-chain aware: evicts leaf blocks first, preserving shared prefix chains. Subsumes the preemption-priority behavior the old hand-rolled LRUEvictor::push_front used to provide.
• Lru — plain recency-based LRU.
• MultiLru — 4-tier frequency-aware LRU built on a TinyLFU tracker.

All three give the same “suffix blocks evicted before shared prefixes” outcome that the old evictor was designed to produce; Lineage does it structurally (via the block parent chain) rather than via monotonic counters.

Sequence Tracking

Each active request is tracked as a sequence, managing its token blocks and generation state. As tokens are generated, the sequence tracks which blocks are partial (MutableBlock<G1>, still being filled) versus full (ImmutableBlock<G1>, complete and hashable for prefix caching). When a partial block fills up, it gets “promoted” to a full block with a content-based SequenceHash (or collapses onto an existing registered handle if the PLH is already present), enabling future cache hits from requests with matching prefixes.

Performance Model

The mocker supports three timing prediction modes:

Polynomial Model (Default): Uses hardcoded polynomial formulas that approximate typical GPU behavior. Prefill time scales quadratically with token count, while decode time depends on the total active KV cache size.

Interpolated Model: Loads actual profiling data from an NPZ file containing measured prefill and decode latencies. The mocker interpolates between data points to predict timing for any input size. This enables high-fidelity simulation matching a specific hardware configuration.

AIC Model (--aic-perf-model): Uses the NVIDIA AI Configurator (AIC) SDK for latency prediction. AIC provides calibrated performance models for specific GPU/model/engine combinations, predicting prefill and decode latency as a function of batch size, sequence length, and prefix cache hits. The model path is automatically derived from --model-path, and the engine type from --engine-type. This mode is opt-in and requires both the aiconfigurator SDK and loadable systems/perf data for the requested tuple.

Bootstrap Rendezvous (Disaggregated Serving)

For disaggregated prefill/decode deployments, prefill and decode workers coordinate via a simple TCP-based rendezvous protocol. The decode worker connects to the prefill worker’s bootstrap port and waits until the prefill phase completes and KV cache is ready. Either side can arrive first—the rendezvous completes when both are ready.

KV Transfer Latency Simulation

The mocker simulates KV cache transfer time between prefill and decode workers. Before the prefill worker emits its first (and only) token, it sleeps for a duration based on:

• kv_bytes_per_token (auto-computed from model config): num_layers * 2 * num_kv_heads * head_dim * dtype_bytes. The dtype_bytes is determined by --kv-cache-dtype: when set to auto (default), it uses the model’s dtype from config; when explicitly set (e.g., fp8), it uses the specified dtype instead. It can also be overridden directly with --kv-bytes-per-token.
• kv_transfer_bandwidth (default: 64.0 GB/s, inter-node InfiniBand)
• Transfer time: num_input_tokens * kv_bytes_per_token / bandwidth

This delay is injected after the scheduler’s prefill compute simulation completes, modeling the sequential flow: prefill computation → KV transfer → decode begins. Set --kv-transfer-bandwidth 0 to disable.

Integration with Dynamo

KV Event Publishing

When prefix caching is enabled, the mocker publishes KV cache events to the distributed runtime. These events notify the system when blocks are stored (new content cached) or removed (evicted). This enables the KV-aware router to make intelligent routing decisions based on which workers have which prefixes cached.

Metrics Publishing

Each scheduler publishes metrics about its current state, including the number of active decode blocks per DP rank. The router uses these metrics for load-aware routing decisions.

Testing Scenarios

The mocker is particularly useful for:

1. Router Testing - Validate KV-aware routing without GPUs
2. Planner Testing - Test SLA-based planners with realistic timing
3. Fault Tolerance - Test request migration, graceful shutdown
4. Disaggregation - Test P/D separation and KV transfer coordination
5. Performance Modeling - Prototype scheduling policies
6. CI/CD - Fast integration tests without hardware dependencies

Comparison with Real Engines

Next Steps

Feature Gaps (WIP)

For the broader mocker enhancement roadmap, see #6383.

The following features are not yet supported by the mocker:

• Multi-tier memory - No support for offloading KV cache to CPU/disk or onboarding back to GPU; potential future integration with KVBM
• Multimodal support - Currently only simulates text token processing; no vision encoder or cross-attention simulation
• Native Rust reference counting - Work in progress to use native Rc/Arc for block reference counting, enabling natural RAII patterns for simpler tracking