{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"bitsandbytes","slug":"bitsandbytes","name":"bitsandbytes","type":"repo","url":"https://github.com/bitsandbytes-foundation/bitsandbytes","page_url":"https://unfragile.ai/bitsandbytes","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"bitsandbytes__cap_0","uri":"capability://data.processing.analysis.8.bit.block.wise.optimizer.quantization.with.memory.efficient.training","name":"8-bit block-wise optimizer quantization with memory-efficient training","description":"Implements block-wise quantization (blocksize=256) of optimizer states during training, reducing memory footprint by ~75% through the Adam8bit, AdamW8bit, and PagedAdamW optimizer classes. Uses a QuantState management system to track quantization metadata (absmax scaling factors, bit-width) separately from quantized weights, enabling efficient gradient updates without full dequantization. Integrates with PyTorch's optim.Optimizer interface via GlobalOptimManager for transparent state management across distributed training (FSDP).","intents":["Train large language models on GPUs with limited VRAM by reducing optimizer state memory","Fine-tune 7B+ parameter models on consumer GPUs (24GB VRAM) without model parallelism","Maintain training speed while reducing memory overhead of Adam/AdamW optimizers"],"best_for":["ML engineers fine-tuning LLMs on resource-constrained hardware","Teams running distributed training with FSDP across multiple GPUs","Researchers prototyping large-scale training without enterprise GPU clusters"],"limitations":["Block-wise quantization introduces ~1-2% accuracy degradation vs full-precision training in some models","Requires CUDA-capable GPU; CPU fallback available but significantly slower","Paged optimizers add ~50-100ms per optimization step due to dynamic memory management","Not compatible with some custom optimizer implementations that bypass PyTorch's standard interfaces"],"requires":["PyTorch 1.12+","CUDA 11.0+ or ROCm 5.0+ (or CPU-only mode)","GPU with minimum 6GB VRAM for practical use","Python 3.8+"],"input_types":["PyTorch model parameters (torch.nn.Parameter)","Gradient tensors (torch.Tensor)","Optimizer hyperparameters (learning_rate, weight_decay, etc.)"],"output_types":["Updated model parameters (quantized state)","Optimizer state metadata (absmax factors, bit-width info)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_1","uri":"capability://data.processing.analysis.llm.int8.mixed.precision.8.bit.inference.with.outlier.handling","name":"llm.int8() mixed-precision 8-bit inference with outlier handling","description":"Performs 8-bit matrix multiplication with automatic mixed-precision handling for outlier features, implemented via Linear8bitLt module that uses vector-wise quantization for weights and dynamic outlier detection. Achieves ~50% memory reduction by quantizing most weights to int8 while keeping high-magnitude outlier columns in float16, then reconstructing outputs through a two-path computation (quantized path + outlier path). Uses custom autograd functions to integrate with PyTorch's backward pass for inference-time fine-tuning.","intents":["Run inference on 13B+ parameter models on single consumer GPUs without quantization-aware training","Reduce model memory footprint for deployment while maintaining near-original accuracy","Enable real-time inference on resource-constrained edge devices"],"best_for":["ML engineers deploying pre-trained LLMs to production with memory constraints","Teams building chatbot/API services on limited GPU infrastructure","Researchers benchmarking inference efficiency without retraining models"],"limitations":["Outlier detection adds ~10-15% latency overhead vs pure int8 inference","Accuracy degradation of 1-3% on some downstream tasks (summarization, QA) vs full-precision","Requires model to be loaded in float32 or float16 first before conversion (temporary 2x memory spike)","Not compatible with models using custom CUDA kernels or non-standard layer types"],"requires":["PyTorch 1.12+","CUDA 11.0+ (int8 GEMM support via cuBLAS)","Pre-trained model weights (no quantization-aware training needed)","Minimum 8GB GPU VRAM for 13B models"],"input_types":["Pre-trained PyTorch model (nn.Module)","Input tokens/embeddings (torch.Tensor)","Optional: outlier threshold configuration"],"output_types":["Logits or hidden states (torch.Tensor, float32)","Quantization metadata (outlier indices, scaling factors)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_10","uri":"capability://data.processing.analysis.nf4.normal.float.4.bit.quantization.with.information.theoretic.optimality","name":"nf4 (normal float 4-bit) quantization with information-theoretic optimality","description":"Implements NF4 quantization data type that is information-theoretically optimal for normally-distributed weights, using a fixed set of 16 quantization levels derived from the inverse normal CDF. Achieves better accuracy than standard FP4 quantization on transformer weights by allocating more quantization levels to high-probability regions of the normal distribution. Integrates with QLoRA training to quantize base model weights while keeping LoRA adapters in full precision.","intents":["Quantize transformer model weights with minimal accuracy loss using distribution-aware quantization","Fine-tune 70B+ models on consumer GPUs with better accuracy than FP4 quantization","Reduce model memory footprint while maintaining task performance"],"best_for":["ML engineers fine-tuning large language models with QLoRA","Teams requiring high-accuracy quantization for downstream tasks","Researchers exploring distribution-aware quantization schemes"],"limitations":["NF4 assumes normally-distributed weights; performs poorly on non-normal distributions (e.g., some vision models)","Fixed quantization levels cannot adapt to specific model architectures; one-size-fits-all approach","Quantization overhead (computing inverse normal CDF) adds ~5-10ms per layer during quantization","Dequantization requires lookup table; slightly slower than simple FP4 dequantization","Not beneficial for already-quantized models (e.g., post-training quantization); best for QLoRA"],"requires":["PyTorch 1.12+","Pre-trained model with normally-distributed weights (typical for transformers)","CUDA 11.0+ (for efficient quantization/dequantization)","peft library for LoRA integration"],"input_types":["Full-precision model weights (float32, float16)","Quantization configuration (blocksize, double_quant flag)"],"output_types":["NF4-quantized weights (4-bit representation)","Quantization metadata (absmax factors, bit-width)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_11","uri":"capability://data.processing.analysis.double.quantization.of.scaling.factors.for.metadata.compression","name":"double quantization of scaling factors for metadata compression","description":"Implements secondary quantization of absmax scaling factors (used in primary weight quantization), reducing metadata memory footprint by 50-75%. For example, in QLoRA with double quantization, the absmax factors themselves are quantized to int8 using a separate set of scaling factors, creating a two-level quantization hierarchy. Reduces overall model size by compressing the quantization metadata that would otherwise consume significant memory.","intents":["Further reduce model memory footprint by compressing quantization metadata","Enable training of even larger models on limited GPU memory","Reduce checkpoint file sizes for quantized models"],"best_for":["ML engineers training 70B+ models on extremely limited GPU memory","Teams optimizing checkpoint storage and transfer bandwidth","Researchers exploring nested quantization schemes"],"limitations":["Double quantization introduces additional accuracy loss (0.5-1%) on top of primary quantization","Adds complexity to quantization/dequantization logic; harder to debug","Dequantization requires two-level reconstruction; adds ~5-10ms latency per layer","Metadata corruption is harder to detect; requires careful validation","Minimal benefit if model size is not metadata-bound (i.e., weight quantization is primary bottleneck)"],"requires":["PyTorch 1.12+","Primary quantization scheme (e.g., NF4 or FP4)","CUDA 11.0+ for efficient two-level dequantization","Understanding of nested quantization for debugging"],"input_types":["Primary quantized weights (int8, int4, NF4, FP4)","Absmax scaling factors (float32)","Double quantization configuration"],"output_types":["Double-quantized scaling factors (int8)","Secondary scaling factors (float32)","Reconstructed primary scaling factors (float32)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_12","uri":"capability://code.generation.editing.linear4bit.and.linear8bitlt.custom.layer.modules.with.quantization.integration","name":"linear4bit and linear8bitlt custom layer modules with quantization integration","description":"Implements drop-in replacement nn.Module subclasses (Linear4bit, Linear8bitLt, LinearNF4, LinearFP4) that wrap standard PyTorch linear layers with quantization/dequantization logic. Linear4bit uses 4-bit quantization with LoRA adapters for training, while Linear8bitLt uses 8-bit quantization with outlier handling for inference. These modules integrate custom autograd functions to compute gradients through quantized weights, and expose quantization configuration through constructor parameters.","intents":["Replace standard nn.Linear layers with quantized versions without rewriting model code","Enable quantized training/inference by swapping layer types in model definitions","Maintain compatibility with standard PyTorch model architectures and training loops"],"best_for":["ML engineers converting existing models to use quantized layers","Teams building quantized model architectures from scratch","Researchers prototyping quantization schemes with minimal code changes"],"limitations":["Custom layers add ~10-20% training time overhead vs standard nn.Linear","Quantization configuration must be specified per-layer; no automatic layer detection","Incompatible with some PyTorch features (torch.jit.script, torch.compile in some cases)","Debugging quantized layers requires understanding of custom autograd functions","Not compatible with some model architectures (e.g., models with custom CUDA kernels)"],"requires":["PyTorch 1.12+","CUDA 11.0+ (for GPU acceleration)","Understanding of nn.Module API","Quantization configuration (bit-width, blocksize, dtype)"],"input_types":["Input tensor (torch.Tensor, float32 or float16)","Quantization configuration (in_features, out_features, bias, quant_type, etc.)"],"output_types":["Output tensor (torch.Tensor, float32 or float16)","Quantized weight tensor (int8, int4, NF4, FP4)","QuantState metadata"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_13","uri":"capability://data.processing.analysis.cpu.optimization.fallbacks.for.quantization.operations","name":"cpu optimization fallbacks for quantization operations","description":"Implements CPU-based fallback implementations for quantization/dequantization and GEMM operations when CUDA is unavailable or for specific operations not yet ported to GPU. Uses NumPy/PyTorch CPU operations to perform quantization with block-wise or vector-wise scaling, enabling bitsandbytes to work on CPU-only systems at the cost of 50-100x slower performance. Automatically selects CPU fallback when GPU implementation is unavailable.","intents":["Enable bitsandbytes usage on CPU-only systems for development and testing","Provide graceful degradation when GPU libraries are unavailable","Support CI/CD pipelines that test on CPU before GPU deployment"],"best_for":["Developers testing quantization logic on laptops without GPUs","CI/CD pipelines running unit tests on CPU","Teams prototyping quantization schemes before GPU optimization"],"limitations":["CPU implementations are 50-100x slower than GPU; not practical for production inference","CPU memory usage is higher than GPU (no memory-efficient quantization on CPU)","Some quantization schemes (NF4, double quantization) are not implemented on CPU","CPU fallback may silently produce incorrect results if not properly tested","Training on CPU is prohibitively slow; only suitable for small models and testing"],"requires":["PyTorch 1.12+ with CPU support","NumPy (for CPU quantization operations)","Sufficient CPU RAM (2-3x model size)"],"input_types":["Full-precision tensors (torch.Tensor on CPU)","Quantization configuration"],"output_types":["Quantized tensors (on CPU)","Dequantized tensors (on CPU)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_2","uri":"capability://data.processing.analysis.qlora.4.bit.quantization.with.nf4.fp4.data.types.and.lora.adapters","name":"qlora 4-bit quantization with nf4/fp4 data types and lora adapters","description":"Enables parameter-efficient fine-tuning of 4-bit quantized models by combining NF4 (Normal Float 4-bit, information-theoretically optimal for normally-distributed weights) or FP4 quantization with LoRA low-rank adapters. Implements Linear4bit, LinearNF4, and LinearFP4 modules that quantize base model weights to 4-bit while keeping LoRA adapter weights in full precision, achieving ~75% memory reduction. Uses double quantization (secondary quantization of absmax scaling factors) to further compress metadata, and integrates custom autograd functions to compute gradients only through the LoRA adapters during backpropagation.","intents":["Fine-tune 70B+ parameter models on single 24GB GPUs with LoRA adapters","Reduce training memory footprint to enable multi-GPU fine-tuning on consumer hardware","Maintain adapter portability by keeping base model weights frozen and quantized"],"best_for":["ML engineers fine-tuning large open-source models (Llama, Mistral) on limited budgets","Teams building multi-task systems with shared base models and task-specific adapters","Researchers exploring parameter-efficient fine-tuning at scale"],"limitations":["4-bit quantization introduces 2-5% accuracy loss on some tasks (code generation, reasoning) vs full-precision fine-tuning","LoRA rank/alpha hyperparameters require tuning; no automatic selection","Inference requires loading base model in 4-bit format; cannot use standard model checkpoints directly","Gradient computation through quantized weights adds ~20-30% training time overhead vs full-precision LoRA","Double quantization metadata adds complexity; requires careful handling in distributed training"],"requires":["PyTorch 1.12+","CUDA 11.0+ or ROCm 5.0+","Pre-trained model in float32/float16 (will be converted to 4-bit)","peft library for LoRA integration (optional but recommended)","Minimum 16GB GPU VRAM for 70B models"],"input_types":["Pre-trained PyTorch model (nn.Module)","Training data (input_ids, attention_mask, labels)","LoRA configuration (rank, alpha, target_modules)","Quantization config (nf4=True/False, double_quant=True/False)"],"output_types":["LoRA adapter weights (torch.Tensor, float32)","Quantized base model weights (4-bit representation)","Training logs (loss, perplexity)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_3","uri":"capability://tool.use.integration.dynamic.library.loading.with.multi.backend.support.cuda.rocm.cpu","name":"dynamic library loading with multi-backend support (cuda/rocm/cpu)","description":"Implements a five-layer architecture where Layer 4 handles dynamic library loading and backend detection, automatically selecting between CUDA, ROCm, XPU, and CPU implementations at runtime based on available hardware. Uses ctypes-based FFI bindings to load compiled .so/.dll binaries and register operators with PyTorch's dispatcher, enabling transparent backend switching without code changes. Includes fallback mechanisms: if CUDA library fails to load, automatically attempts ROCm, then CPU implementations.","intents":["Deploy bitsandbytes across heterogeneous hardware (NVIDIA GPUs, AMD GPUs, Intel GPUs, CPUs) from single codebase","Enable graceful degradation when GPU libraries unavailable (fallback to slower CPU path)","Support development workflows across different machines without recompilation"],"best_for":["ML teams with mixed hardware infrastructure (some NVIDIA, some AMD GPUs)","Open-source projects requiring broad hardware compatibility","CI/CD pipelines testing across multiple GPU architectures"],"limitations":["CPU fallback is 50-100x slower than GPU implementations; not practical for production inference","Requires pre-compiled binaries for each backend; building from source is complex","Library loading failures produce cryptic error messages if binaries are missing or incompatible","ROCm support lags CUDA in feature completeness (some operators missing)","XPU backend is experimental and may have stability issues"],"requires":["CUDA 11.0+ (for NVIDIA) OR ROCm 5.0+ (for AMD) OR CPU-only mode","Matching CUDA/ROCm version to compiled binaries (version mismatch causes silent failures)","Python 3.8+","ctypes library (standard in Python)"],"input_types":["Hardware detection queries (GPU type, compute capability)","Operator registration requests (function name, signature)"],"output_types":["Loaded library handle (ctypes.CDLL)","Registered operator functions (callable)","Backend name (string: 'cuda', 'rocm', 'cpu')"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_4","uri":"capability://code.generation.editing.custom.autograd.functions.for.quantized.backward.passes","name":"custom autograd functions for quantized backward passes","description":"Implements custom PyTorch autograd functions (torch.autograd.Function subclasses) that define forward and backward passes for quantized operations, enabling gradient computation through quantized layers without full dequantization. For example, Linear4bit.backward() computes gradients only through LoRA adapters while treating quantized base weights as frozen, using stored quantization metadata (absmax, bit-width) to reconstruct intermediate values efficiently. Integrates with PyTorch's autograd tape to support gradient accumulation, mixed-precision training, and distributed gradient synchronization.","intents":["Enable backpropagation through quantized layers without materializing full-precision intermediates","Support gradient accumulation and mixed-precision training with quantized models","Maintain numerical stability during backward passes through quantized weights"],"best_for":["ML engineers training quantized models with gradient checkpointing or mixed-precision","Researchers implementing custom quantization schemes with PyTorch autograd","Teams using distributed training (DDP/FSDP) with quantized models"],"limitations":["Custom autograd functions add ~10-20% training time overhead vs native PyTorch ops","Gradient computation requires storing quantization metadata in memory (absmax, bit-width per block)","Backward pass through quantized weights is numerically different from full-precision (introduces ~0.1-0.5% gradient noise)","Incompatible with some PyTorch features (torch.jit.script, torch.compile in some cases)","Debugging gradient flow requires understanding quantization-specific autograd logic"],"requires":["PyTorch 1.12+ with autograd support","Understanding of torch.autograd.Function API","Quantization metadata stored during forward pass (QuantState objects)","CUDA kernels for efficient dequantization in backward pass"],"input_types":["Input tensors (torch.Tensor)","Quantized weight tensors (int8/int4)","QuantState metadata (absmax, bit-width, blocksize)","Gradient tensors from downstream layers"],"output_types":["Gradients w.r.t. inputs (torch.Tensor)","Gradients w.r.t. quantized weights (sparse or aggregated)","Gradients w.r.t. LoRA adapters (torch.Tensor, full-precision)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_5","uri":"capability://data.processing.analysis.quantstate.management.for.quantization.metadata.tracking","name":"quantstate management for quantization metadata tracking","description":"Implements a QuantState class that encapsulates quantization metadata (absmax scaling factors, bit-width, blocksize, data type) separately from quantized tensor data, enabling efficient state management across forward/backward passes and distributed training. QuantState objects are attached to quantized tensors as attributes, allowing gradient computation to access quantization parameters without materializing full-precision weights. Integrates with PyTorch's parameter storage to support serialization, checkpointing, and FSDP synchronization.","intents":["Track quantization parameters (absmax, bit-width) separately from quantized weights for efficient memory usage","Enable checkpoint/resume workflows with quantized models by serializing QuantState metadata","Support distributed training by synchronizing QuantState across GPUs in FSDP"],"best_for":["ML engineers implementing custom quantization schemes with PyTorch","Teams training quantized models with checkpointing and resume workflows","Researchers exploring quantization-aware training with distributed setups"],"limitations":["QuantState metadata adds ~1-2% memory overhead (absmax factors, bit-width per block)","Serialization of QuantState requires custom pickle/checkpoint logic; not compatible with standard torch.save()","FSDP synchronization of QuantState adds ~5-10ms per training step in distributed settings","Debugging QuantState corruption is difficult; requires manual inspection of metadata"],"requires":["PyTorch 1.12+","Understanding of quantization parameters (absmax, blocksize, bit-width)","Custom checkpoint/serialization code for QuantState objects","FSDP or DDP for distributed training"],"input_types":["Quantized tensor data (int8/int4)","Quantization parameters (absmax, bit-width, blocksize, dtype)","Checkpoint/serialization requests"],"output_types":["QuantState objects (metadata containers)","Serialized checkpoint data (dict with metadata)","Synchronized QuantState across distributed ranks"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_6","uri":"capability://data.processing.analysis.quantization.and.dequantization.operations.with.configurable.bit.widths","name":"quantization and dequantization operations with configurable bit-widths","description":"Implements low-level quantization/dequantization kernels (in bitsandbytes/functional.py) that convert between full-precision tensors and quantized representations (int8, int4, NF4, FP4) using configurable block sizes and scaling strategies. Supports vector-wise quantization (per-column scaling for weights) and block-wise quantization (per-block scaling for optimizer states), with absmax-based scaling to preserve outliers. Provides both CUDA kernel implementations (Layer 5) and Python wrappers (Layer 3) that dispatch to appropriate backend.","intents":["Convert model weights from float32/float16 to int8/int4 for memory-efficient storage","Dequantize weights on-the-fly during inference without materializing full-precision copies","Support different quantization schemes (NF4 for weights, FP4 for gradients) with configurable parameters"],"best_for":["ML engineers implementing custom quantization schemes","Teams building inference engines with quantized models","Researchers benchmarking different quantization strategies"],"limitations":["Quantization introduces 1-3% accuracy loss depending on bit-width and data distribution","Dequantization adds latency: ~5-10ms per layer for int8, ~10-20ms for int4 on typical GPUs","Block-wise quantization requires careful blocksize selection (256 is default); wrong choice degrades accuracy","NF4 quantization assumes normally-distributed weights; performs poorly on non-normal distributions","CPU implementations are 50-100x slower than CUDA; not practical for production"],"requires":["PyTorch 1.12+","CUDA 11.0+ (for GPU acceleration) or CPU-only mode","Input tensors must be contiguous in memory","Quantization parameters (blocksize, bit-width, scaling strategy)"],"input_types":["Full-precision tensors (float32, float16)","Quantization configuration (bit-width, blocksize, dtype)","Quantized tensors (int8, int4) for dequantization"],"output_types":["Quantized tensors (int8, int4, NF4, FP4)","Scaling factors (absmax per block)","Dequantized tensors (float32, float16)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_7","uri":"capability://data.processing.analysis.matrix.multiplication.with.quantized.operands.gemm.operations","name":"matrix multiplication with quantized operands (gemm operations)","description":"Implements efficient matrix multiplication (GEMM) operations where one or both operands are quantized (int8 or int4), using CUDA kernels that avoid full dequantization. For example, int8 GEMM computes C = A_dequant(Q_A, scale_A) @ B_dequant(Q_B, scale_B) where dequantization happens on-the-fly within the kernel, reducing memory bandwidth. Supports mixed-precision output (float32, float16) and integrates with PyTorch's autograd for gradient computation through quantized operands.","intents":["Perform matrix multiplications with quantized weights without materializing full-precision intermediates","Reduce memory bandwidth during inference by keeping weights in quantized form","Support efficient training through quantized layers with gradient computation"],"best_for":["ML engineers building inference engines for quantized models","Teams optimizing memory bandwidth-bound operations (transformer attention, linear layers)","Researchers benchmarking quantized inference performance"],"limitations":["int8 GEMM requires CUDA compute capability 7.0+ (Volta or newer); older GPUs fall back to slower implementations","int4 GEMM is slower than int8 due to bit-packing overhead; typically 30-50% slower than int8","Quantization-induced accuracy loss propagates through matrix multiplications; can compound in deep networks","GEMM kernels are optimized for specific tensor shapes; irregular shapes may trigger slow fallback paths","Mixed-precision output (float32) requires additional conversion; float16 output is faster"],"requires":["CUDA 11.0+ with int8 GEMM support (cuBLAS or custom kernels)","NVIDIA GPU with compute capability 7.0+ (Volta, Turing, Ampere, Ada)","Quantized weight tensors with known scaling factors","PyTorch 1.12+"],"input_types":["Quantized weight tensor (int8 or int4)","Input tensor (float32, float16, or quantized)","Scaling factors (absmax per block)","Bias tensor (optional, float32)"],"output_types":["Output tensor (float32 or float16)","Gradient tensors (for backward pass)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_8","uri":"capability://data.processing.analysis.paged.optimizer.state.management.for.memory.efficient.updates","name":"paged optimizer state management for memory-efficient updates","description":"Implements PagedAdamW optimizer that uses paged memory allocation for optimizer states, storing only the current page of states in GPU memory and paging out older pages to CPU RAM. Reduces GPU memory footprint by 50-75% compared to standard AdamW by keeping optimizer states (momentum, variance) on CPU and only loading the current batch's states onto GPU during updates. Uses a custom memory manager to handle page swapping with minimal overhead.","intents":["Train very large models (100B+) on limited GPU memory by offloading optimizer states to CPU","Reduce GPU memory pressure in multi-GPU training setups","Enable longer training runs without running out of GPU memory"],"best_for":["ML engineers training 100B+ parameter models on limited GPU clusters","Teams with high CPU-to-GPU bandwidth (NVLink, PCIe 4.0+) for efficient paging","Researchers exploring memory-efficient training at extreme scale"],"limitations":["Paging overhead adds 20-50ms per optimization step due to CPU-GPU data transfer","Requires sufficient CPU RAM (typically 2-3x model size); insufficient CPU RAM causes thrashing","Page swapping introduces non-determinism in training (different page ordering = different convergence)","Incompatible with some distributed training setups (FSDP with gradient accumulation)","CPU-GPU bandwidth becomes bottleneck; only beneficial with high-bandwidth interconnects (NVLink)"],"requires":["PyTorch 1.12+","CUDA 11.0+","Sufficient CPU RAM (2-3x model size)","High-bandwidth CPU-GPU interconnect (PCIe 4.0+ or NVLink recommended)","Minimum 16GB GPU VRAM"],"input_types":["Model parameters (torch.nn.Parameter)","Gradient tensors","Optimizer hyperparameters (learning_rate, weight_decay)","Page size configuration"],"output_types":["Updated model parameters","Paged optimizer states (on CPU and GPU)","Training metrics (loss, throughput)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__cap_9","uri":"capability://automation.workflow.fsdp.integration.for.distributed.quantized.model.training","name":"fsdp integration for distributed quantized model training","description":"Integrates bitsandbytes quantized layers with PyTorch's Fully Sharded Data Parallel (FSDP) training, enabling distributed training of quantized models across multiple GPUs/nodes. Implements custom hooks in GlobalOptimManager to synchronize QuantState metadata across ranks, and ensures quantized parameters are properly sharded and gathered during forward/backward passes. Supports gradient accumulation and mixed-precision training with quantized models in FSDP.","intents":["Train quantized models across multiple GPUs using FSDP without custom distributed code","Scale quantized training to multi-node setups with automatic gradient synchronization","Maintain quantization efficiency while leveraging distributed training benefits"],"best_for":["ML teams training large quantized models across GPU clusters","Researchers exploring distributed training of memory-efficient models","Organizations with multi-GPU infrastructure (8+ GPUs) training 70B+ models"],"limitations":["FSDP synchronization of QuantState adds 5-10ms per training step overhead","Requires careful configuration of FSDP sharding strategy; wrong choice causes memory imbalance","Gradient accumulation with FSDP and quantization is complex; requires custom hooks","Debugging distributed training failures is difficult; requires understanding of FSDP and quantization","Not compatible with some FSDP features (CPU offloading with quantized layers)"],"requires":["PyTorch 1.12+ with FSDP support","CUDA 11.0+ with multi-GPU support","Distributed training setup (torch.distributed)","Multiple GPUs (minimum 2, typically 8+)","High-bandwidth inter-GPU communication (NVLink or fast Ethernet)"],"input_types":["Quantized PyTorch model (nn.Module with quantized layers)","Training data (distributed DataLoader)","FSDP configuration (sharding_strategy, cpu_offload, etc.)","Quantization configuration"],"output_types":["Trained quantized model (sharded across ranks)","Synchronized QuantState metadata","Training logs (loss, throughput, communication overhead)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bitsandbytes__headline","uri":"capability://model.training.8.bit.and.4.bit.quantization.library.for.pytorch","name":"8-bit and 4-bit quantization library for pytorch","description":"Bitsandbytes is a lightweight library designed for efficient 8-bit and 4-bit quantization of PyTorch models, enabling memory-efficient training and inference of large language models on limited GPU resources.","intents":["best quantization library for PyTorch","4-bit quantization for large language models","8-bit model training tools","efficient inference for large models","quantization solutions for limited GPU memory"],"best_for":["developers working with large language models","users needing efficient memory usage"],"limitations":[],"requires":["PyTorch"],"input_types":["PyTorch 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":["PyTorch 1.12+","CUDA 11.0+ or ROCm 5.0+ (or CPU-only mode)","GPU with minimum 6GB VRAM for practical use","Python 3.8+","CUDA 11.0+ (int8 GEMM support via cuBLAS)","Pre-trained model weights (no quantization-aware training needed)","Minimum 8GB GPU VRAM for 13B models","Pre-trained model with normally-distributed weights (typical for transformers)","CUDA 11.0+ (for efficient quantization/dequantization)","peft library for LoRA integration"],"failure_modes":["Block-wise quantization introduces ~1-2% accuracy degradation vs full-precision training in some models","Requires CUDA-capable GPU; CPU fallback available but significantly slower","Paged optimizers add ~50-100ms per optimization step due to dynamic memory management","Not compatible with some custom optimizer implementations that bypass PyTorch's standard interfaces","Outlier detection adds ~10-15% latency overhead vs pure int8 inference","Accuracy degradation of 1-3% on some downstream tasks (summarization, QA) vs full-precision","Requires model to be loaded in float32 or float16 first before conversion (temporary 2x memory spike)","Not compatible with models using custom CUDA kernels or non-standard layer types","NF4 assumes normally-distributed weights; performs poorly on non-normal distributions (e.g., some vision models)","Fixed quantization levels cannot adapt to specific model architectures; one-size-fits-all approach","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":"2026-06-17T09:51:04.690Z","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=bitsandbytes","compare_url":"https://unfragile.ai/compare?artifact=bitsandbytes"}},"signature":"iW+abetHfMLErwmBmu4j6O0tKNOusOsixYcSmy35PR2UiIbr/VYva5W2WsZ3d2S5bGExsP56BubbFKbf8uJiAw==","signedAt":"2026-06-19T22:10:07.603Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bitsandbytes","artifact":"https://unfragile.ai/bitsandbytes","verify":"https://unfragile.ai/api/v1/verify?slug=bitsandbytes","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"}}