Phase 6: Quantization and Inference Optimization for EMEP

Phase 6 delivers the QuantizationEngine and InferenceBackend, enabling FP16, BF16, FP8, INT8, and INT4 quantization with performance validation for efficient model serving.

Phase 6 focuses on making evolved models efficient enough for production deployment. In this phase, you implement the QuantizationEngine to compress model weights into reduced precision formats, and the InferenceBackend to serve those compressed models with optimized latency and throughput. The deliverables include support for FP16, BF16, FP8, INT8, and INT4 quantization schemes, plus a validation pipeline that verifies accuracy is preserved within acceptable bounds after compression.

Purpose

The purpose of Phase 6 is to bridge the gap between high-quality merged models and deployable artifacts. Evolution may produce large models that are too slow or memory-intensive for production. Quantization reduces memory footprint and accelerates inference, while the InferenceBackend provides the runtime layer for executing quantized models on target hardware.

Entry Criteria

Phase 6 begins when the following entry criteria are satisfied:

• Phase 5 (Evolution Engine) is complete and produces merged models with documented fitness scores
• The ModelRegistry can store both full-precision and quantized model variants
• The ArtifactStore supports binary artifact versioning
• Target deployment hardware profiles are defined (GPU generation, CPU AVX support, accelerator type)
• Accuracy tolerance thresholds are agreed upon for each quantization level

Exit Criteria

Phase 6 is complete when all exit criteria are met:

• The QuantizationEngine supports FP16, BF16, FP8, INT8, and INT4 formats
• Each quantization path includes calibration or dynamic range estimation
• The InferenceBackend loads and executes quantized models
• Performance validation confirms latency, throughput, and memory targets
• Accuracy regression is within agreed thresholds for each quantization level
• The ArtifactStore stores both the original and quantized artifacts with lineage links

Primary Components

Component	Path	Role in Phase 6
QuantizationEngine	/deployment/quantization	Compresses model weights to reduced precision formats
InferenceBackend	/deployment/inference	Runtime for executing quantized models on target hardware
ModelRegistry	/tracking/model-registry	Indexes full-precision and quantized variants
ArtifactStore	/tracking/artifact-registry	Stores quantized binary artifacts
BenchmarkEngine	/evaluation/benchmark-specification	Measures inference latency and throughput
EvaluationEngine	/evaluation/framework	Validates accuracy after quantization

Deliverables

The following deliverables are produced in Phase 6:

1. QuantizationEngine: Supports FP16, BF16, FP8, INT8, and INT4 quantization with per-layer and per-tensor granularity
2. Calibration pipeline: Static and dynamic calibration for INT8 and FP8 quantization ranges
3. InferenceBackend: Hardware-optimized runtime supporting CUDA, ROCm, and CPU backends
4. Performance validation suite: Automated benchmarking of latency, throughput, and memory usage per hardware profile
5. Accuracy regression tests: Compares quantized outputs against full-precision baselines on benchmark datasets
6. Quantization-aware merge guidance: Recommendations from the EvolutionEngine that favor merge strategies compatible with aggressive quantization

Dependencies

Phase 6 depends on the completion of earlier phases and specific components:

• Phase 5: Requires evolved models as input for quantization
• Phase 4: Requires benchmark datasets and evaluation protocols to validate post-quantization accuracy
• Phase 1: Requires the ModelRegistry to track model variants
• Hardware profiles: Must be defined before inference backend optimization targets are set

Key Tasks

The detailed task breakdown for Phase 6 is maintained in the project task tracker. See Phase 6 Tasks for assigned owners, estimates, and current status.

Risks

The primary risks in Phase 6 include accuracy collapse from aggressive INT4 quantization, backend-specific inference bugs, and hardware incompatibility with newer formats like FP8. These risks and their mitigations are documented in the Risk Register.

Quality Gate

Before Phase 6 can be marked complete, the following quality gate must pass:

• INT8 quantization achieves less than 1% accuracy regression on the primary benchmark
• FP16 inference latency is at least 1.5x faster than FP32 on target GPU hardware
• The InferenceBackend successfully loads and runs at least one model from each supported quantization format
• Performance validation results are reproducible across three independent runs
• All quantized artifacts are signed and stored in the ArtifactStore

Position in Roadmap

Phase 6 follows the Evolution Engine and prepares models for the deployment pipeline. It transforms research-quality models into production-ready artifacts that meet latency and memory constraints.