llmcompressor
FrameworkFreeToolkit for LLM quantization, pruning, and distillation.
Capabilities16 decomposed
one-shot post-training quantization with calibration-free execution
Medium confidenceApplies quantization algorithms (GPTQ, AWQ, AutoRound) to pre-trained models in a single forward pass without requiring fine-tuning, using a modifier-based system that injects quantization observers into the model graph during a calibration phase. The framework traces model execution sequentially, collecting activation statistics, then applies learned quantization parameters to weights and activations with minimal accuracy loss.
Uses a modifier-based architecture where quantization logic is injected as PyTorch hooks into the model graph, enabling algorithm-agnostic calibration and composition of multiple compression techniques (quantization + pruning + distillation) in a single pipeline without model rewriting
Faster than AutoGPTQ or GPTQ-for-LLaMA because it abstracts algorithm selection and calibration into reusable modifiers, allowing parallel experimentation; more flexible than ONNX Runtime quantization because it preserves PyTorch semantics and integrates directly with vLLM
multi-algorithm quantization scheme composition
Medium confidenceEnables mixing of different quantization algorithms (GPTQ for weights, AWQ for activations, SmoothQuant for layer normalization) within a single compression recipe, applying algorithm-specific modifiers to different layer types based on a declarative YAML specification. The modifier system resolves dependencies between algorithms and applies them in topologically-sorted order during the compression session.
Implements a declarative modifier system where quantization algorithms are pluggable components that can be composed and targeted to specific layer patterns (e.g., 'all attention layers', 'decoder blocks 10-20') without code changes, using a dependency-aware execution engine
More composable than monolithic quantization tools like GPTQ-for-LLaMA because algorithms are decoupled; more transparent than AutoML quantization because users explicitly define which algorithms apply where
distributed compression for models exceeding single-gpu memory
Medium confidenceEnables compression of very large models (100B+) across multiple GPUs using distributed calibration and modifier application. The framework partitions the model across GPUs, coordinates calibration data flow, synchronizes quantization parameters across devices, and reconstructs the full model for export, supporting both data parallelism and model parallelism strategies.
Implements distributed compression by partitioning models across GPUs, coordinating calibration data flow, and synchronizing quantization parameters across devices, enabling compression of models 2-3x larger than single-GPU capacity without requiring distributed training infrastructure
More practical than distributed training because it only requires calibration, not full retraining; more efficient than sequential processing because it parallelizes across GPUs; more flexible than cloud quantization services because it runs on-premises
fine-tuning with compression for accuracy recovery
Medium confidenceEnables training models with compression modifiers active, allowing weights to adapt to quantization constraints during fine-tuning. The framework applies quantization-aware training (QAT) by injecting fake quantization operations into the forward pass, computing gradients through quantized weights, and updating parameters to minimize loss while respecting quantization constraints.
Implements quantization-aware training by injecting fake quantization operations into the forward pass and enabling gradient flow through quantized weights, allowing models to adapt to quantization constraints during fine-tuning without requiring separate QAT frameworks
More integrated than separate QAT tools because compression modifiers are active during training; more flexible than fixed QAT schemes because any compression recipe can be used; more practical than retraining from scratch because it starts from a compressed checkpoint
model-free post-training quantization without model loading
Medium confidenceEnables quantization of models without loading the full model into memory, using a model-free approach that analyzes model structure from metadata and applies quantization based on layer statistics. The framework reads model weights on-demand, computes quantization parameters, and writes quantized weights back without keeping the full model in memory, suitable for extremely large models or resource-constrained environments.
Implements model-free quantization by reading and processing weights on-demand without loading the full model into memory, enabling quantization of models 10-100x larger than available VRAM by streaming weights from disk
More memory-efficient than standard quantization because it never loads the full model; more practical than distributed quantization for single-machine setups; more flexible than cloud quantization services because it runs locally
mixture of experts (moe) model compression with expert-level targeting
Medium confidenceProvides specialized compression support for MoE models by enabling per-expert quantization, pruning, and distillation. The framework identifies expert layers, applies compression modifiers to individual experts or expert groups, and preserves routing logic, enabling efficient compression of sparse MoE architectures where only a subset of experts are active per token.
Implements MoE-aware compression by identifying expert layers, applying per-expert quantization and pruning, and preserving routing logic, enabling efficient compression of sparse architectures where only a subset of experts are active per token
More suitable for MoE models than generic compression because it preserves expert structure; more efficient than compressing MoE as dense models because it exploits sparsity; better integrated with vLLM than generic sparse tensor libraries
multimodal model compression with vision-language alignment
Medium confidenceExtends compression to multimodal models (vision-language models) by applying compression to vision encoders, text encoders, and fusion layers while preserving cross-modal alignment. The framework handles different modality-specific compression strategies (e.g., more aggressive quantization for vision encoders) and validates that compressed models maintain alignment between vision and language representations.
Implements multimodal compression by applying modality-specific compression strategies to vision encoders, text encoders, and fusion layers while validating cross-modal alignment, enabling efficient compression of vision-language models without degrading multimodal understanding
More suitable for multimodal models than generic compression because it preserves cross-modal alignment; more flexible than single-modality compression because it handles heterogeneous architectures; better integrated with multimodal inference engines than generic tools
compression metrics and accuracy evaluation framework
Medium confidenceProvides built-in evaluation tools for measuring compression impact on model accuracy, including task-specific metrics (perplexity, BLEU, exact match), benchmark datasets (MMLU, HellaSwag, TruthfulQA), and comparison utilities for quantifying accuracy loss. The framework integrates with HuggingFace Evaluate and supports custom evaluation functions, enabling systematic assessment of compression quality.
Implements integrated evaluation framework with support for standard benchmarks (MMLU, HellaSwag, TruthfulQA), task-specific metrics (perplexity, BLEU), and custom evaluation functions, enabling systematic accuracy assessment without external evaluation tools
More convenient than manual evaluation because benchmarks are pre-configured; more flexible than fixed metrics because custom functions are supported; more integrated than external evaluation tools because it's built into the compression pipeline
sequential model tracing and subgraph execution for memory-constrained compression
Medium confidenceDecomposes large models into sequential subgraphs (e.g., individual transformer layers) and processes them one at a time, keeping only the current subgraph in GPU memory while offloading others to disk or CPU. The framework traces model execution using PyTorch's symbolic tracing, identifies layer boundaries, and reconstructs activations on-demand during calibration, enabling compression of models larger than GPU VRAM.
Implements layer-by-layer sequential onloading where the model graph is decomposed into subgraphs, each processed independently with automatic activation reconstruction, enabling compression of models 2-3x larger than GPU VRAM without distributed training infrastructure
More practical than distributed quantization (DeepSpeed, FSDP) for single-GPU setups because it avoids communication overhead; more memory-efficient than naive batch processing because it streams activations to disk rather than buffering entire model
gptq weight quantization with hessian-based optimization
Medium confidenceImplements GPTQ (Generative Pre-trained Transformer Quantization) algorithm that quantizes model weights to low bit-widths (INT4, INT3, INT2) by solving per-layer least-squares problems using Hessian information from the calibration data. The algorithm iteratively quantizes weights while updating remaining weights to minimize reconstruction error, achieving near-lossless compression with minimal calibration data.
Implements Hessian-aware quantization where weight importance is determined by second-order Fisher information from calibration data, enabling per-channel and per-group quantization with automatic sensitivity-based bit-width selection
More accurate than simple magnitude-based quantization because it accounts for weight interactions; faster than full retraining because Hessian computation is one-shot; more flexible than fixed-bit-width schemes because it supports mixed precision
awq activation-aware weight quantization
Medium confidenceImplements Activation-aware Weight Quantization (AWQ) which identifies and preserves activation outliers by smoothing weight distributions before quantization. The algorithm analyzes activation ranges across calibration data, identifies channels with extreme values, and applies per-channel scaling to reduce outlier impact, enabling lower bit-widths while maintaining accuracy.
Implements activation-aware quantization by analyzing per-channel activation ranges during calibration and applying learned scaling factors to weight distributions before quantization, enabling INT4 weights with better accuracy than magnitude-based approaches
More accurate than GPTQ for INT4 because it explicitly handles activation outliers; more efficient than SmoothQuant because it doesn't require activation quantization, only weight smoothing
structured and unstructured pruning with layer-wise sparsity patterns
Medium confidenceApplies structured (removing entire channels/heads) and unstructured (removing individual weights) pruning to reduce model parameters, using a modifier system that targets specific layer patterns and applies sparsity masks. The framework supports magnitude-based pruning, gradient-based pruning, and learned sparsity patterns, with automatic mask generation and application during model inference.
Implements layer-wise pruning through a modifier system that applies sparsity masks to specific layer patterns, supporting both structured (channel/head removal) and unstructured (weight removal) pruning with automatic importance estimation from calibration data
More flexible than magnitude-based pruning because it supports learned importance scores; more practical than gradient-based pruning because it doesn't require training; better integrated with vLLM than generic sparse tensor libraries
smoothquant activation smoothing for mixed-precision quantization
Medium confidenceImplements SmoothQuant algorithm that smooths activation distributions by transferring quantization difficulty from activations to weights through learned per-channel scaling. The algorithm identifies activation channels with extreme ranges, applies inverse scaling to weights, and forward scaling to activations, enabling lower-precision activation quantization while maintaining weight precision.
Implements activation smoothing by learning per-channel scaling factors that transfer quantization difficulty from activations to weights, enabling INT8 activation quantization without accuracy loss by exploiting weight flexibility
More effective than naive INT8 quantization because it explicitly handles activation outliers; more practical than full retraining because smoothing is one-shot; better for mixed-precision than SmoothQuant alone because it combines weight and activation quantization
autoround learned quantization with gradient-based parameter optimization
Medium confidenceImplements AutoRound algorithm that learns optimal quantization parameters (scales, zero-points, rounding) through gradient-based optimization on calibration data. The algorithm treats quantization as a differentiable operation, computes gradients with respect to quantization parameters, and iteratively updates them to minimize reconstruction error, achieving better accuracy than fixed quantization schemes.
Implements gradient-based quantization parameter learning where scales, zero-points, and rounding modes are optimized through backpropagation on calibration data, treating quantization as a differentiable operation rather than a fixed transformation
More accurate than GPTQ for INT4 because it optimizes all quantization parameters jointly; more flexible than AWQ because it learns parameters end-to-end; slower but higher quality than one-shot quantization
compression recipe specification and execution engine
Medium confidenceProvides a declarative YAML-based recipe system for specifying compression pipelines, where users define modifiers (quantization, pruning, distillation), targets (layer patterns), and parameters without writing code. The execution engine parses recipes, resolves modifier dependencies, validates compatibility, and orchestrates the compression session, enabling reproducible and shareable compression workflows.
Implements a declarative recipe system where compression pipelines are specified in YAML with modifier definitions, layer targets (using regex patterns), and parameters, with an execution engine that resolves dependencies and validates compatibility before running compression
More reproducible than imperative compression code because recipes are version-controlled; more accessible than low-level APIs because non-experts can modify recipes; more flexible than fixed compression tools because recipes can be customized per model
vllm-native model export with quantization metadata preservation
Medium confidenceExports compressed models in a format optimized for vLLM inference, preserving quantization metadata (scales, zero-points, bit-widths) in safetensors format with custom JSON metadata. The exporter ensures compatibility with vLLM's quantization kernels, validates that exported models can be loaded and inferred, and provides fallback options for unsupported quantization schemes.
Implements vLLM-native export that preserves quantization metadata in safetensors format with custom JSON extensions, enabling direct loading into vLLM without intermediate conversion while validating compatibility with vLLM's quantization kernels
Faster than generic model export because it's optimized for vLLM's quantization format; more reliable than manual metadata management because validation is automatic; more portable than pickle-based formats because safetensors is language-agnostic
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓ML engineers deploying large models to resource-constrained environments
- ✓teams needing rapid model optimization without access to training compute
- ✓researchers comparing quantization algorithm effectiveness
- ✓ML engineers fine-tuning compression strategies for domain-specific models
- ✓researchers exploring algorithm combinations for optimal accuracy-efficiency tradeoffs
- ✓teams with heterogeneous hardware (some GPUs support FP8, others don't)
- ✓teams with multi-GPU infrastructure compressing very large models
- ✓organizations deploying 100B+ models where single-GPU compression is infeasible
Known Limitations
- ⚠Requires representative calibration dataset (typically 128-512 samples); poor dataset selection degrades accuracy
- ⚠Sequential tracing adds memory overhead proportional to model size; distributed compression needed for >70B models
- ⚠Quantization parameters are static post-compression; no dynamic per-batch adaptation
- ⚠Some model architectures (custom attention, sparse operations) may require custom modifier implementations
- ⚠Algorithm interactions are not automatically validated; incompatible combinations (e.g., two conflicting weight quantizers) require manual recipe debugging
- ⚠Modifier ordering matters but is implicit in YAML; circular dependencies or missing dependencies can cause silent failures
Requirements
Input / Output
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About
Neural Magic's toolkit for compressing large language models through quantization, pruning, and distillation techniques, producing optimized models that run efficiently on CPUs and GPUs with minimal accuracy loss.
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