{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"autogptq","slug":"autogptq","name":"AutoGPTQ","type":"repo","url":"https://github.com/AutoGPTQ/AutoGPTQ","page_url":"https://unfragile.ai/autogptq","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"autogptq__cap_0","uri":"capability://data.processing.analysis.gptq.based.weight.only.quantization.with.configurable.bit.precision","name":"gptq-based weight-only quantization with configurable bit precision","description":"Implements the GPTQ algorithm to convert full-precision model weights to 2/3/4/8-bit integer representations while preserving activation precision, using per-group quantization with configurable group sizes (typically 128) and optional activation description (desc_act) for improved accuracy. The quantization process performs layer-wise calibration on sample data, computing optimal quantization scales and zero-points to minimize reconstruction error without requiring gradient updates.","intents":["Reduce model memory footprint from FP16 to 4-bit for deployment on consumer GPUs","Quantize a 70B parameter model to fit within 24GB VRAM constraints","Configure quantization parameters (bit-width, group size, desc_act) for accuracy vs speed tradeoffs","Calibrate quantization on domain-specific text samples to preserve task performance"],"best_for":["ML engineers optimizing inference cost and latency on NVIDIA/AMD GPUs","Researchers benchmarking quantization impact on model quality","Teams deploying large models on resource-constrained hardware"],"limitations":["Quantization is weight-only; activations remain FP16/FP32, limiting memory savings vs full quantization","Requires representative calibration data (typically 128-1024 samples); poor calibration data degrades accuracy","No support for dynamic quantization; quantization parameters are static post-calibration","macOS not supported; requires Linux or Windows with NVIDIA/AMD/Intel GPUs"],"requires":["Python 3.8+","PyTorch 2.x compatible","NVIDIA GPU (Maxwell+) with CUDA 11.8+ OR AMD GPU with ROCm 5.4.2+","Representative calibration dataset (text samples matching model's training distribution)"],"input_types":["pretrained model (HuggingFace format)","quantization config (bit precision, group size, desc_act flag)","calibration dataset (text samples)"],"output_types":["quantized model weights (int2/int3/int4/int8)","quantization metadata (scales, zero-points per group)","model checkpoint (HuggingFace compatible format)"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_1","uri":"capability://code.generation.editing.multi.backend.quantized.inference.with.hardware.specific.kernels","name":"multi-backend quantized inference with hardware-specific kernels","description":"Provides pluggable backend implementations (CUDA, Exllama/ExllamaV2, Marlin, Triton, ROCm, HPU) that execute quantized matrix multiplications with specialized kernels optimized for different hardware. The framework abstracts backend selection through a factory pattern (AutoGPTQForCausalLM), automatically selecting the fastest available kernel based on GPU architecture and quantization parameters, with fallback chains for compatibility.","intents":["Run quantized inference 25-50% faster than FP16 baseline on NVIDIA GPUs","Execute quantized models on AMD GPUs using ROCm without code changes","Deploy quantized inference on Intel Gaudi 2 HPUs for enterprise inference","Automatically select the optimal kernel (Marlin for int4*fp16, Exllama for int4, etc.) based on hardware"],"best_for":["Production inference teams requiring sub-100ms latency on quantized models","Multi-GPU deployment scenarios with heterogeneous hardware (NVIDIA + AMD)","Organizations with Intel Gaudi or custom accelerator infrastructure"],"limitations":["Marlin kernel requires NVIDIA compute capability 8.0+ (Ampere or newer); older GPUs fall back to CUDA kernels with lower performance","Exllama kernels optimized for int4 only; other bit precisions use generic CUDA kernels","Backend selection is automatic; manual kernel override not exposed in high-level API","ROCm support requires specific ROCm versions (5.4.2, 5.6, 5.7); version mismatches cause silent fallback to slower kernels"],"requires":["NVIDIA GPU (Maxwell+) with CUDA 11.8+ OR AMD GPU with ROCm 5.4.2+ OR Intel Gaudi 2","Quantized model weights in AutoGPTQ format","PyTorch 2.x with GPU support"],"input_types":["quantized model checkpoint","input tokens (int64 tensor)","generation parameters (max_length, temperature, top_p)"],"output_types":["generated token sequences","logits (optional)","generation metadata (timing, tokens/sec)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_10","uri":"capability://text.generation.language.quantization.aware.generation.with.token.by.token.inference","name":"quantization-aware generation with token-by-token inference","description":"Implements efficient token-by-token generation for quantized models using the generate() API, which performs single-token inference in a loop with quantized matrix multiplications. The generation pipeline handles KV-cache management, attention mask computation, and sampling (greedy, top-k, top-p, temperature) while maintaining quantized weight efficiency throughout generation.","intents":["Generate text from a quantized model with standard sampling strategies (temperature, top-k, top-p)","Stream generated tokens in real-time for chat applications","Control generation behavior (max_length, stop sequences, repetition penalty) on quantized models","Benchmark generation speed (tokens/sec) on quantized vs FP16 models"],"best_for":["Production chat/text generation systems using quantized models for cost efficiency","Real-time inference applications requiring low latency per token","Streaming inference scenarios where token-by-token output is required"],"limitations":["Token-by-token generation is slower than batch inference for multiple sequences; no built-in batching optimization","KV-cache is not quantized; it remains FP16/FP32, limiting memory savings during generation","Generation parameters (temperature, top-k, top-p) are applied in Python, not GPU kernels; sampling is a CPU bottleneck for high-throughput scenarios","No support for speculative decoding or other advanced generation optimizations"],"requires":["Quantized model checkpoint","Input tokens (prompt)","Generation config (max_length, temperature, top_p, etc.)"],"input_types":["input_ids (int64 tensor)","attention_mask (optional)","generation_config (dict or GenerationConfig object)"],"output_types":["generated_ids (int64 tensor)","generation_metrics (tokens/sec, latency)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_11","uri":"capability://memory.knowledge.quantization.config.serialization.and.reproducibility","name":"quantization config serialization and reproducibility","description":"Serializes quantization parameters (bit precision, group size, desc_act, calibration config) to JSON config files that are saved alongside model checkpoints, enabling reproducible quantization and easy sharing of quantization settings. The config format is compatible with HuggingFace's config.json structure, allowing quantized models to be loaded with standard HuggingFace APIs.","intents":["Save quantization config alongside model checkpoint for reproducible quantization","Share quantization settings with team members or community without re-quantizing","Version-control quantization parameters alongside model weights","Load quantized models with standard HuggingFace APIs (AutoModel.from_pretrained) without custom code"],"best_for":["Teams requiring reproducible quantization pipelines with version control","Open-source communities sharing quantized models with standardized configs","Production systems needing audit trails of quantization parameters"],"limitations":["Quantization config does not include calibration dataset; reproducibility requires storing calibration data separately","Config format is AutoGPTQ-specific; quantized models cannot be loaded by other frameworks (GGUF, bitsandbytes)","No config validation; invalid configs may fail silently during model loading","Quantization config versioning is manual; no automatic migration for config format changes"],"requires":["Quantized model checkpoint","Quantization parameters (bit precision, group size, desc_act)"],"input_types":["quantization config (dict or QuantizationConfig object)","model config (HuggingFace format)"],"output_types":["quantization_config.json (JSON file)","config.json (HuggingFace model config with quantization metadata)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_2","uri":"capability://code.generation.editing.multi.architecture.model.support.with.factory.based.instantiation","name":"multi-architecture model support with factory-based instantiation","description":"Provides specialized quantized model implementations for 40+ architectures (Llama, Mistral, Falcon, Qwen, Yi, etc.) through an AutoGPTQForCausalLM factory that detects model architecture from HuggingFace config and instantiates the appropriate subclass (e.g., LlamaGPTQForCausalLM, MistralGPTQForCausalLM). Each architecture implementation overrides quantized linear layer definitions and attention mechanisms to match the original model's structure while using quantized weights.","intents":["Quantize a Llama-2-70B model without writing architecture-specific code","Load a quantized Mistral model and run inference with automatic architecture detection","Add support for a new model architecture by implementing a custom quantized linear layer","Ensure quantized models maintain compatibility with original model APIs (generate, forward, etc.)"],"best_for":["Teams quantizing diverse model families (Meta, Mistral, Qwen, etc.) with single API","Researchers adding quantization support for new model architectures","Production systems requiring model-agnostic quantization pipelines"],"limitations":["Custom model architectures require implementing a new subclass; no automatic code generation for unsupported models","Architecture detection relies on HuggingFace model_type field; models with non-standard configs may fail detection","Fused attention modules (e.g., flash-attention) are architecture-specific and not available for all models","PEFT-LoRA fine-tuning support varies by architecture; some models have limited or no PEFT integration"],"requires":["HuggingFace model config with standard model_type field","PyTorch model definition compatible with AutoGPTQ's quantized linear layer API","For custom architectures: understanding of model's linear layer structure and attention mechanisms"],"input_types":["model name (HuggingFace hub ID or local path)","model config (JSON from HuggingFace)","pretrained weights (safetensors or PyTorch format)"],"output_types":["quantized model instance (AutoGPTQForCausalLM subclass)","model config with quantization metadata","generation outputs (tokens, logits)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_3","uri":"capability://data.processing.analysis.calibration.based.quantization.with.sample.driven.scale.computation","name":"calibration-based quantization with sample-driven scale computation","description":"Performs layer-wise quantization calibration by passing representative samples through the model, computing optimal quantization scales and zero-points for each weight group to minimize reconstruction error. The calibration process uses Hessian-based optimization (from GPTQ paper) to determine per-group scales that preserve model accuracy, with support for custom calibration datasets and configurable sample counts (typically 128-1024 samples).","intents":["Calibrate quantization on domain-specific text (e.g., medical, code) to preserve task-specific accuracy","Use fewer calibration samples (128) for faster quantization vs more samples (1024) for higher accuracy","Understand how calibration data quality impacts quantized model performance","Reproduce quantization results with fixed random seeds and calibration datasets"],"best_for":["Teams with domain-specific models requiring calibration on in-domain text","Researchers studying quantization accuracy vs calibration sample count tradeoffs","Production pipelines needing reproducible quantization with fixed calibration data"],"limitations":["Calibration requires representative samples matching model's training distribution; random text degrades accuracy","Calibration is computationally expensive (requires forward passes through all layers); typically takes 10-60 minutes on 70B models","No adaptive calibration; quantization parameters are fixed post-calibration and cannot adjust per-input","Calibration dataset size is fixed; no online/streaming calibration support"],"requires":["Calibration dataset (text samples, typically 128-1024 examples)","GPU with sufficient VRAM for forward passes (FP16 precision during calibration)","Tokenizer matching the model's training tokenizer"],"input_types":["pretrained model (FP16/FP32)","calibration dataset (raw text or tokenized sequences)","quantization config (bit precision, group size)"],"output_types":["per-group quantization scales (float32)","per-group zero-points (int32)","quantized weights (int2/int3/int4/int8)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_4","uri":"capability://code.generation.editing.peft.lora.fine.tuning.integration.for.quantized.models","name":"peft-lora fine-tuning integration for quantized models","description":"Enables parameter-efficient fine-tuning of quantized models using LoRA (Low-Rank Adaptation) by freezing quantized weights and adding trainable low-rank adapter modules. The integration handles quantized weight compatibility with PEFT's LoRA implementation, allowing gradient-based fine-tuning on quantized models without dequantizing weights, reducing memory overhead during training.","intents":["Fine-tune a quantized 70B model on task-specific data using only 2-5% additional parameters","Reduce fine-tuning memory from 80GB (full model) to 20GB (quantized + LoRA) on a single GPU","Adapt a quantized base model to multiple downstream tasks with separate LoRA adapters","Merge LoRA weights back into quantized model for deployment without additional parameters"],"best_for":["Teams fine-tuning quantized models on limited GPU memory (24-48GB)","Multi-task learning scenarios requiring task-specific adapters on shared quantized base","Cost-sensitive fine-tuning where reducing parameter count is critical"],"limitations":["PEFT-LoRA support is architecture-specific; not all 40+ supported models have full PEFT integration","LoRA adapters add inference latency (~5-10%) due to additional matrix multiplications","Quantized weights cannot be updated during fine-tuning; only LoRA adapters are trainable","Merging LoRA weights back into quantized model requires dequantization, increasing model size"],"requires":["PEFT library (peft>=0.4.0)","Quantized model checkpoint","Fine-tuning dataset with labels","GPU with sufficient VRAM for gradient computation (typically 24GB+ for 70B models)"],"input_types":["quantized model","LoRA config (rank, alpha, target modules)","fine-tuning dataset (text + labels)"],"output_types":["trained LoRA adapters (low-rank weight matrices)","merged model (quantized weights + LoRA)","fine-tuning metrics (loss, accuracy)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_5","uri":"capability://automation.workflow.fused.attention.module.optimization.for.quantized.models","name":"fused attention module optimization for quantized models","description":"Implements fused attention kernels (e.g., flash-attention) that combine attention computation (query-key-dot-product, softmax, value-multiplication) into a single GPU kernel, reducing memory bandwidth and improving inference speed. Fused attention is architecture-specific and integrated into quantized model implementations where supported, automatically replacing standard attention with optimized kernels during inference.","intents":["Reduce attention computation latency by 30-50% through kernel fusion on quantized models","Lower peak memory usage during inference by avoiding intermediate attention matrix materialization","Maintain numerical stability in attention computation while using quantized weights","Enable longer context lengths on memory-constrained GPUs by reducing attention memory footprint"],"best_for":["Production inference systems optimizing for latency-sensitive workloads (chat, real-time translation)","Long-context inference scenarios (8K+ tokens) on consumer GPUs","Batch inference where attention memory is a bottleneck"],"limitations":["Fused attention support is architecture-specific; only available for Llama, Mistral, and a few other models","Requires NVIDIA GPU with compute capability 7.5+ (Turing or newer); older GPUs fall back to standard attention","Fused attention may have slightly different numerical behavior than standard attention due to kernel implementation","Not compatible with all attention variants (e.g., multi-query attention, grouped query attention) in all models"],"requires":["NVIDIA GPU with compute capability 7.5+ (Turing, Ampere, Ada, etc.)","CUDA 11.8+","Quantized model with fused attention support (architecture-dependent)"],"input_types":["quantized model with fused attention implementation","input tokens and attention masks"],"output_types":["attention outputs (same shape as standard attention)","inference timing metrics"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_6","uri":"capability://memory.knowledge.huggingface.model.hub.integration.with.quantized.model.sharing","name":"huggingface model hub integration with quantized model sharing","description":"Enables seamless integration with HuggingFace Hub for uploading and downloading quantized models, automatically handling model config serialization, quantization metadata (scales, zero-points), and weight format conversion. Quantized models can be pushed to Hub with a single API call and loaded by other users without requiring quantization code, treating quantized models as first-class HuggingFace artifacts.","intents":["Upload a quantized model to HuggingFace Hub for community sharing and reuse","Download a pre-quantized model from Hub and run inference without quantization code","Version-control quantized model checkpoints alongside original model configs","Enable reproducible quantization by sharing calibration configs and quantization metadata"],"best_for":["Open-source ML communities sharing quantized models (e.g., TheBloke's quantized models)","Teams distributing quantized models internally via private HuggingFace Hub instances","Researchers publishing quantized model variants alongside papers"],"limitations":["Quantized model size is still large (4-bit model ~25% of FP16 size); Hub storage quotas may limit uploads","Quantization metadata (scales, zero-points) adds ~5-10% overhead to model size","No automatic quantization config versioning; users must manually track quantization parameters","Cross-framework compatibility is limited; quantized models are AutoGPTQ-specific and not compatible with GGUF or other formats"],"requires":["HuggingFace Hub account with write access","HuggingFace Hub API token (huggingface_hub library)","Quantized model checkpoint in AutoGPTQ format"],"input_types":["quantized model checkpoint","model config (JSON)","quantization metadata (scales, zero-points)"],"output_types":["HuggingFace Hub model card","quantized model files (safetensors or PyTorch format)","quantization config (JSON)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_7","uri":"capability://data.processing.analysis.evaluation.framework.for.quantized.model.accuracy.assessment","name":"evaluation framework for quantized model accuracy assessment","description":"Provides built-in evaluation tasks (language modeling, text classification, multiple-choice QA) to benchmark quantized model accuracy against FP16 baselines, measuring perplexity, accuracy, and F1 scores. The evaluation framework supports standard datasets (WikiText, LAMBADA, HellaSwag) and custom evaluation tasks, enabling systematic accuracy comparison before and after quantization.","intents":["Measure quantization accuracy loss (e.g., 0.5% perplexity increase) before production deployment","Compare accuracy across different quantization configs (4-bit vs 3-bit, group size 128 vs 256)","Validate that domain-specific calibration improves quantized model accuracy on target tasks","Generate quantization accuracy reports for stakeholder review"],"best_for":["ML teams validating quantization impact on model quality before deployment","Researchers benchmarking quantization methods across model families","Quality assurance pipelines requiring automated accuracy regression testing"],"limitations":["Evaluation tasks are limited to language modeling, classification, and QA; custom task evaluation requires manual implementation","Evaluation datasets are fixed (WikiText, LAMBADA, HellaSwag); no support for custom dataset integration without code changes","Evaluation is computationally expensive (requires full model inference on large datasets); typically takes 1-4 hours per model","No statistical significance testing; results are point estimates without confidence intervals"],"requires":["Quantized model checkpoint","Evaluation dataset (downloaded automatically or provided manually)","GPU with sufficient VRAM for inference (typically 24GB+ for 70B models)"],"input_types":["quantized model","evaluation task (language modeling, classification, QA)","evaluation dataset (WikiText, LAMBADA, HellaSwag, or custom)"],"output_types":["accuracy metrics (perplexity, accuracy, F1)","comparison vs FP16 baseline","evaluation report (JSON or CSV)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_8","uri":"capability://code.generation.editing.custom.model.architecture.support.with.extensible.quantized.layer.api","name":"custom model architecture support with extensible quantized layer api","description":"Provides an extensible framework for adding quantization support to custom or unsupported model architectures by implementing a custom quantized linear layer class that inherits from BaseQuantizedLinearLayer. The framework handles weight loading, quantization parameter management, and kernel selection, allowing architecture-specific implementations to focus on layer structure and attention mechanisms.","intents":["Add quantization support for a proprietary or research model architecture not in the 40+ supported list","Implement custom quantized linear layers with model-specific optimizations (e.g., grouped query attention)","Extend AutoGPTQ to support new quantization methods beyond GPTQ (e.g., AWQ, GGUF)","Integrate custom kernels (e.g., Triton, custom CUDA) with AutoGPTQ's quantization pipeline"],"best_for":["Researchers developing novel model architectures requiring quantization support","Teams with proprietary models needing quantization without waiting for official support","Developers extending AutoGPTQ with new quantization algorithms or kernels"],"limitations":["Requires deep understanding of model architecture and quantized linear layer API; non-trivial implementation effort","Custom implementations may not benefit from optimized kernels (Marlin, Exllama); fallback to generic CUDA kernels","No automatic testing framework for custom implementations; developers must write their own tests","Custom architectures are not eligible for community support; maintainers may not review or merge contributions"],"requires":["Understanding of model architecture (linear layer structure, attention mechanisms)","Knowledge of AutoGPTQ's BaseQuantizedLinearLayer API","Python 3.8+, PyTorch 2.x","Optional: CUDA knowledge for custom kernel implementation"],"input_types":["model architecture definition (PyTorch module)","quantization config (bit precision, group size)","calibration dataset"],"output_types":["custom quantized model class (inherits from BaseGPTQForCausalLM)","quantized weights and metadata","inference outputs"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__cap_9","uri":"capability://automation.workflow.cuda.and.rocm.kernel.compilation.with.automatic.backend.selection","name":"cuda and rocm kernel compilation with automatic backend selection","description":"Provides build infrastructure for compiling optimized CUDA kernels (for NVIDIA GPUs) and ROCm kernels (for AMD GPUs) from source, with automatic backend detection and fallback chains. The build system detects GPU architecture at installation time and compiles appropriate kernels, enabling single-wheel distributions that work across NVIDIA and AMD hardware without manual kernel selection.","intents":["Install AutoGPTQ on NVIDIA GPU and automatically compile CUDA kernels for the detected GPU architecture","Deploy AutoGPTQ on AMD GPU with ROCm without manual kernel configuration","Build AutoGPTQ from source with custom CUDA/ROCm versions for specific hardware","Troubleshoot kernel compilation errors and fallback to generic implementations"],"best_for":["DevOps teams deploying AutoGPTQ across heterogeneous GPU clusters (NVIDIA + AMD)","Researchers building custom kernels or optimizing for specific GPU architectures","Organizations with strict hardware requirements (e.g., specific CUDA version for security)"],"limitations":["Kernel compilation requires CUDA toolkit or ROCm SDK installed; pre-built wheels may not include all kernel variants","Compilation time is significant (10-30 minutes); users may prefer pre-built wheels over source builds","ROCm support is limited to specific versions (5.4.2, 5.6, 5.7); version mismatches cause silent fallback to slower kernels","Marlin kernel requires NVIDIA compute capability 8.0+; older GPUs silently fall back to CUDA kernels without warning"],"requires":["NVIDIA CUDA toolkit 11.8+ (for NVIDIA GPUs) OR AMD ROCm 5.4.2+ (for AMD GPUs)","C++ compiler (gcc, clang, MSVC)","Python 3.8+, setuptools, wheel","Optional: CMake 3.20+ for advanced build configuration"],"input_types":["AutoGPTQ source code","CUDA/ROCm toolkit","GPU architecture specification (optional)"],"output_types":["compiled CUDA/ROCm kernels (.so/.dll files)","Python wheel (.whl) with kernels embedded","build logs and diagnostics"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autogptq__headline","uri":"capability://model.training.llm.quantization.library","name":"llm quantization library","description":"AutoGPTQ is a user-friendly library for quantizing large language models, enabling efficient model inference with reduced memory requirements while maintaining performance across various architectures.","intents":["best LLM quantization library","LLM quantization for fast inference","how to quantize models with AutoGPTQ","efficient model quantization solutions","quantization libraries for large language models"],"best_for":["developers working with large language models","users needing efficient inference on limited hardware"],"limitations":[],"requires":["NVIDIA or AMD GPUs"],"input_types":["full-precision models"],"output_types":["quantized models"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.x compatible","NVIDIA GPU (Maxwell+) with CUDA 11.8+ OR AMD GPU with ROCm 5.4.2+","Representative calibration dataset (text samples matching model's training distribution)","NVIDIA GPU (Maxwell+) with CUDA 11.8+ OR AMD GPU with ROCm 5.4.2+ OR Intel Gaudi 2","Quantized model weights in AutoGPTQ format","PyTorch 2.x with GPU support","Quantized model checkpoint","Input tokens (prompt)","Generation config (max_length, temperature, top_p, etc.)"],"failure_modes":["Quantization is weight-only; activations remain FP16/FP32, limiting memory savings vs full quantization","Requires representative calibration data (typically 128-1024 samples); poor calibration data degrades accuracy","No support for dynamic quantization; quantization parameters are static post-calibration","macOS not supported; requires Linux or Windows with NVIDIA/AMD/Intel GPUs","Marlin kernel requires NVIDIA compute capability 8.0+ (Ampere or newer); older GPUs fall back to CUDA kernels with lower performance","Exllama kernels optimized for int4 only; other bit precisions use generic CUDA kernels","Backend selection is automatic; manual kernel override not exposed in high-level API","ROCm support requires specific ROCm versions (5.4.2, 5.6, 5.7); version mismatches cause silent fallback to slower kernels","Token-by-token generation is slower than batch inference for multiple sequences; no built-in batching optimization","KV-cache is not quantized; it remains FP16/FP32, limiting memory savings during generation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=autogptq","compare_url":"https://unfragile.ai/compare?artifact=autogptq"}},"signature":"t3YhqXnXs1Mj3sQBi2lgRohP/0qf6GHjR+WBD8Tz5AY8LkINFZfB0lXX0J63YdyaTZyt2aXhxqJsOt7VOnb+AA==","signedAt":"2026-06-21T06:33:10.082Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/autogptq","artifact":"https://unfragile.ai/autogptq","verify":"https://unfragile.ai/api/v1/verify?slug=autogptq","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}