AISimulate

AISimulate predicts LLM serving behavior and searches for strong deployment configurations offline, without bringing up a GPU serving cluster.

Website · E2E Accuracy Overview · FPE Support Matrix · Legacy AIC Support Matrix

AISimulate is the successor to the AIConfigurator (AIC) repository. It brings the complete AIC application and estimator into one standalone home with Dynamo-independent Replay and Sweeper capabilities.

The performance-modeling methodology is described in AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving.

Install

Engine-only

Install AISimulate by itself to use the built-in simulation engine without a Dynamo dependency:

python3 -m pip install aisimulate
aisimulate --help

With Dynamo

Install AISimulate with Dynamo to enable the dynamo runner plus Dynamo-owned Router and Planner configuration adapters:

python3 -m pip install aisimulate ai-dynamo
aisimulate predict --help

AISimulate remains the CLI owner in both profiles. Select the integration at runtime with --stack dynamo; installing Dynamo does not add another simulation command.

Upgrade from standalone AIConfigurator

Remove the former standalone distributions first so that only AISimulate owns the compatibility imports and command:

python3 -m pip uninstall -y aiconfigurator aiconfigurator-core
python3 -m pip install --upgrade aisimulate

Predict one deployment

predict evaluates one pinned deployment configuration. Save this example as prediction.yaml:

engine:
  mode: aggregated
  model: Qwen/Qwen3-32B-FP8
  hardware: h200_sxm
  backend: vllm
  workers:
    aggregated: {}

Engine-only prediction

aisimulate predict \
  --stack engine \
  --config prediction.yaml \
  --output-dir ./aisimulate-prediction

Dynamo-integrated prediction

aisimulate predict \
  --stack dynamo \
  --config prediction.yaml \
  --output-dir ./aisimulate-dynamo-prediction

The CLI prints a concise summary and writes the selected runner's complete report to <output-dir>/prediction.json. Add --capture-per-request to also write requests.jsonl, or use --format json for machine-readable standard output.

Recommend a deployment

recommend searches the prediction schema plus search domains and an optimization goal. Save this example as recommendation.yaml:

engine:
  mode: aggregated
  model: Qwen/Qwen3-32B-FP8
  hardware: auto
  backend: {choices: [vllm, sglang]}
  workers:
    aggregated:
      parallelism: {preset: default}

optimization:
  target: throughput_per_gpu
  hardware: h200_sxm
  constraints:
    max_candidate_gpus: 8

Engine-only recommendation

aisimulate recommend \
  --stack engine \
  --config recommendation.yaml \
  --output-dir ./aisimulate-recommendation

Dynamo-integrated recommendation

aisimulate recommend \
  --stack dynamo \
  --config recommendation.yaml \
  --output-dir ./aisimulate-dynamo-recommendation

Each file under <output-dir>/recommendations/ is a fully materialized, concrete configuration. It contains no search domains and can be passed directly back to predict:

aisimulate predict \
  --config ./aisimulate-recommendation/recommendations/0001.yaml \
  --output-dir ./aisimulate-best-prediction

Both commands support --set PATH=YAML_VALUE, --output-dir, --overwrite, and --format table|json. See the AISimulate CLI User Guide for the complete schema, traffic models, search domains, presets, outputs, and error contract.

AIConfigurator compatibility CLI

The aisimulate wheel preserves the established aiconfigurator command for workflows that have not yet moved to the unified CLI. AISimulate 0.12.0 keeps this compatibility surface, while new prediction and search integrations should start with aisimulate predict and aisimulate recommend.

# Check whether a model/system combination is supported.
aiconfigurator cli support \
  --model-path Qwen/Qwen3-32B-FP8 \
  --system h200_sxm

# Generate a starting deployment configuration.
aiconfigurator cli generate \
  --model-path Qwen/Qwen3-32B-FP8 \
  --total-gpus 8 \
  --system h200_sxm

The compatibility CLI preserves six workflows:

Mode	Purpose
default	Compare aggregated and disaggregated candidates and select a strong starting point
estimate	Estimate one explicitly configured deployment
recommend	Find the minimum GPU count and configuration for a load target and SLA
exp	Run custom experiments from YAML
generate	Generate deployment artifacts without a parameter sweep
support	Check model and system coverage

Read the Legacy AIC CLI User Guide for command examples and the AIC CLI and Python API overview for the complete compatibility surface. The AIC migration guide explains which AIC workflows map to predict or recommend and which ones must continue using the compatibility command for now.

AIConfigurator repository transition

The standalone AIConfigurator repository will publish its final 0.12.0 aiconfigurator and aiconfigurator-core artifacts and then be archived. AISimulate is the canonical home for ongoing development, releases, issues, and pull requests; open all new issues and pull requests in this repository.

The aiconfigurator compatibility command remains available from the aisimulate wheel in 0.12.0. It is targeted for removal in AISimulate 0.13.0, after every remaining AIC workflow has a verified replacement in the unified aisimulate CLI. Until then, use the compatibility command for the workflows identified in the migration guide.

Experimental status and validation boundary

Warning

Replay and Sweeper are experimental surfaces intended for evaluation and feedback, not production capacity planning. Their APIs, schemas, search behavior, and output may change without a standard deprecation period.

AISimulate narrows a deployment search and identifies candidates; it does not replace validation on the target hardware. Benchmark shortlisted configurations on a real deployment before making production capacity or SLA decisions.

SDKs

Use the focused SDK documentation instead of treating CLI internals as public APIs:

• Estimator/FPE Python and Rust SDK
• FPM collection-to-prediction workflow
• Replay SDK and artifact contract
• Sweeper SDK
• AIConfigurator compatibility Python API

Support and accuracy

Support coverage and accuracy are separate evidence. A supported cell means a specific path can execute with the required data; it does not establish that the resulting end-to-end prediction is accurate.

Explicit CUDA graph reservation

KV-cache estimation and engine replay accept an optional rank-local cuda_graph_reserved_bytes value. AISimulate subtracts this fixed runtime reservation before allocating KV cache and preserves it when the native replay runtime rematerializes capacity. For SGLang, the value is additional to the graph/runtime headroom already encoded by mem_fraction_static. The default is zero, so existing serialized callers do not change. See the core API contract.

FPE support matrix

The published Forward Pass Engine (FPE) matrix measures strict-native estimator coverage across a curated roster of current models, GPU systems, backends, and backend versions. It probes native prefill, decode-start, decode-end, and mixed forward-pass calls without fallback. It does not certify the CLI, Replay, Sweeper, serving orchestration, or prediction accuracy.

The matrix was introduced in AISimulate PR #41. Nightly CI refreshes the complete matrix at the nightly source SHA before release artifacts advance to Artifactory.

AIC CLI support matrix

The compatibility support matrix covers AIC command-based aggregated and disaggregated workflows by model, system, backend, and backend version:

• Interactive legacy AIC support matrix
• AIC support-matrix data
• Curated model roster

Check one exact cell from the installed package with:

aiconfigurator cli support \
  --model-path Qwen/Qwen3-32B-FP8 \
  --system h200_sxm \
  --backend vllm \
  --backend-version 0.14.0

E2E accuracy overview

The published E2E Accuracy Overview reports TTFT and TPOT error, curve-shape error, and prediction coverage against matched measured-silicon operating points. It is evidence for the measured configurations, not a universal support contract.

Forward-pass accuracy is tracked separately; see the prediction regression and accuracy design and the current silicon anchor set.

Release artifacts and compatibility

Starting with 0.12.0, this repository owns the complete AIConfigurator product surface—not only its native core. The application, CLI, generator, SDK, Collector, tests, documentation, and development tooling live under python/aisimulate/.

This repository produces exactly two release artifacts:

1. aisimulate Python wheel — the application, both console commands, estimator SDK, model/performance data, Replay, Sweeper, and unified native extension;
2. aisimulate-core Rust crate — the native estimator and simulation core for Rust consumers.

It does not publish an aiconfigurator or aiconfigurator-core wheel, a Python aisimulate-core distribution, or an aiconfigurator-core crate. The aisimulate wheel preserves the aiconfigurator, aiconfigurator_core, and aisimulate_core Python import namespaces during the compatibility window.

The AISimulate wheel does not declare Dynamo as an installation dependency. Dynamo-owned Router, Planner, runtime, transport, and live-Mocker integrations consume AISimulate through optional adapters.

Packaged legal-file copies

The root LICENSE and THIRD_PARTY_NOTICES.md are the canonical repository legal files. Because the Python wheel build is rooted at python/aisimulate/, byte-identical copies are retained there so the wheel can declare and distribute them. These copies do not create a separate licensing boundary, and CI fails if either copy differs from its root original. See the artifact contract for the complete packaging contract.

Develop from source

git clone https://github.com/ai-dynamo/aisimulate.git
cd aisimulate

uv venv .venv
source .venv/bin/activate
uv pip install -e ./python/aisimulate

Current performance profiles are checked-in Parquet files, so normal builds and usage do not require Git LFS. Install Git LFS and run git lfs pull only when working with retained legacy *.txt performance assets or their compatibility tests.

Accuracy evidence

The published E2E Accuracy Overview reports matched client-observed TTFT and TPOT accuracy against measured silicon operating points. It keeps accuracy, evidence coverage, and curve-shape error separate and includes a machine-readable aggregate with exact snapshot digests. See the snapshot and regeneration details for evidence provenance and instructions to rebuild the report.

The checked-in snapshot excludes multi-node configurations and applies only to the exact model, hardware, framework, topology, workload, and concurrency cells that were measured. It is not a universal support or deployment-certification claim. Forward-pass accuracy and strict-native estimator coverage remain separate evidence lanes.

Run the repository validation suites with:

cargo test --workspace
python -m pytest -c pytest.ini tests
python -m pytest -c python/aisimulate/pytest.ini python/aisimulate/tests -m "unit or build"

See DEVELOPMENT.md for environment and test details and CONTRIBUTING.md before sending a change.