{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"exllamav2","slug":"exllamav2","name":"ExLlamaV2","type":"repo","url":"https://github.com/turboderp/exllamav2","page_url":"https://unfragile.ai/exllamav2","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"exllamav2__cap_0","uri":"capability://code.generation.editing.exl2.quantized.model.inference.with.dynamic.token.level.bit.allocation","name":"exl2 quantized model inference with dynamic token-level bit allocation","description":"Executes inference on EXL2-quantized models using dynamic per-token bit allocation, where different weight matrices are quantized to different bit depths (2-8 bits) based on sensitivity analysis. The framework loads quantized weights directly into VRAM and performs mixed-precision matrix multiplications, automatically selecting optimal bit widths per layer to balance quality and memory footprint without requiring full dequantization.","intents":["Run a 70B parameter model on a single 24GB consumer GPU with minimal quality loss","Maximize inference throughput while staying within fixed VRAM constraints","Understand which layers in my model are most sensitive to quantization"],"best_for":["Solo developers and researchers running local LLM inference on consumer GPUs","Teams deploying cost-sensitive inference without enterprise GPU clusters","Builders optimizing for latency-critical applications on edge devices"],"limitations":["EXL2 quantization is lossy; quality degrades with aggressive bit reduction (2-3 bits) compared to FP16 baseline","Requires pre-quantized EXL2 model files; cannot quantize arbitrary GGUF or safetensors models in-place","Dynamic bit allocation adds ~5-10% inference overhead vs static quantization due to per-token routing logic","No support for quantizing models larger than available VRAM during inference"],"requires":["NVIDIA GPU with CUDA Compute Capability 6.0+ (GTX 1060 or newer)","CUDA 11.8+ and cuDNN 8.0+","Pre-quantized EXL2 model files (e.g., from HuggingFace hub)","Python 3.8+"],"input_types":["EXL2 quantized model weights (.safetensors or .bin format)","Tokenized input sequences (integer token IDs)","Optional LoRA adapter weights for fine-tuning"],"output_types":["Token logits (float32 per token)","Sampled token IDs","Attention weights (optional, for interpretability)"],"categories":["code-generation-editing","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_1","uri":"capability://code.generation.editing.gptq.quantized.model.inference.with.group.wise.quantization","name":"gptq quantized model inference with group-wise quantization","description":"Loads and executes inference on GPTQ-quantized models using group-wise quantization, where weight matrices are divided into groups and each group is quantized independently with a shared scale factor. The framework performs fused dequantization-and-multiplication operations in GPU kernels to avoid materializing full-precision weights in VRAM, enabling inference on models that would otherwise exceed GPU memory.","intents":["Run GPTQ-quantized open-source models (e.g., TheBloke's quantizations) on consumer GPUs","Leverage existing GPTQ quantizations from HuggingFace without re-quantizing","Achieve faster inference than pure CPU-based GPTQ implementations"],"best_for":["Developers using pre-quantized models from community sources (TheBloke, etc.)","Teams needing compatibility with existing GPTQ model ecosystems","Builders prioritizing inference speed over maximum compression"],"limitations":["GPTQ quality is lower than EXL2 because it uses uniform bit widths per group rather than dynamic allocation","Group size is fixed at quantization time (typically 128); cannot adjust granularity at inference","Requires exact group size match between quantized model and inference kernel; mismatches cause silent numerical errors","No built-in support for mixed-precision groups (e.g., 4-bit + 8-bit in same model)"],"requires":["NVIDIA GPU with CUDA Compute Capability 6.0+","CUDA 11.8+ and cuDNN 8.0+","Pre-quantized GPTQ model files","Python 3.8+"],"input_types":["GPTQ quantized model weights (.safetensors or .bin format)","Tokenized input sequences (integer token IDs)","Optional LoRA adapter weights"],"output_types":["Token logits (float32 per token)","Sampled token IDs"],"categories":["code-generation-editing","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_10","uri":"capability://data.processing.analysis.batch.inference.with.variable.length.sequence.padding.and.masking","name":"batch inference with variable-length sequence padding and masking","description":"Processes multiple sequences of different lengths in a single batch by padding shorter sequences to the longest sequence length and applying attention masks to ignore padding tokens. The framework automatically handles padding, mask generation, and unpadding of outputs, allowing efficient batched inference without manual sequence length management.","intents":["Process multiple sequences in parallel without manual padding and masking","Maximize GPU utilization by batching sequences of different lengths","Reduce per-sequence latency by amortizing GPU kernel launch overhead"],"best_for":["Developers building batch inference pipelines for document processing or QA","Teams optimizing throughput for inference servers handling variable-length inputs","Builders implementing efficient data loading for training or evaluation"],"limitations":["Padding shorter sequences to match the longest sequence increases computation; worst-case overhead is ~50% if batch contains one very long sequence","Attention masking adds ~5-10% overhead due to mask generation and application","No support for ragged tensors or dynamic shapes; all sequences must be padded to the same length","Unpadding outputs requires tracking original sequence lengths; adds complexity to output handling"],"requires":["NVIDIA GPU with sufficient VRAM for largest batch","Python 3.8+"],"input_types":["Multiple tokenized sequences (variable length)","Attention mask (optional; auto-generated if not provided)","Sequence length metadata"],"output_types":["Batched token logits (padded to longest sequence)","Unpadded outputs (original sequence lengths)"],"categories":["data-processing-analysis","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_11","uri":"capability://data.processing.analysis.model.quantization.to.exl2.and.gptq.formats.with.sensitivity.analysis","name":"model quantization to exl2 and gptq formats with sensitivity analysis","description":"Quantizes full-precision models to EXL2 or GPTQ formats by analyzing layer sensitivity to quantization and selecting appropriate bit widths. For EXL2, the framework performs a sensitivity analysis pass to identify which layers tolerate lower bit depths, then quantizes each layer independently. For GPTQ, it uses group-wise quantization with configurable group size and bit width.","intents":["Convert full-precision models to quantized formats for efficient inference","Understand which layers in a model are most sensitive to quantization","Achieve optimal quality-to-memory tradeoff by selecting appropriate bit widths per layer"],"best_for":["Researchers and developers quantizing custom models for local inference","Teams preparing models for deployment on consumer GPUs","Builders optimizing model size and inference speed for edge devices"],"limitations":["Quantization is lossy; quality degrades with aggressive bit reduction (2-3 bits)","Sensitivity analysis requires a calibration dataset; poor calibration data leads to suboptimal bit width selection","Quantization time is significant (hours for large models); not suitable for rapid iteration","Quantized models are not compatible with standard PyTorch; require ExLlamaV2 or other specialized inference frameworks"],"requires":["Full-precision model weights (.safetensors or .bin format)","Calibration dataset (representative samples for sensitivity analysis)","NVIDIA GPU with sufficient VRAM for full-precision model","Python 3.8+"],"input_types":["Full-precision model weights","Calibration data (tokenized sequences)","Quantization parameters (target bit widths, group size, sensitivity threshold)"],"output_types":["Quantized model weights (EXL2 or GPTQ format)","Quantization metadata (bit widths per layer, scale factors, sensitivity scores)"],"categories":["data-processing-analysis","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_12","uri":"capability://tool.use.integration.inference.api.with.openai.compatible.endpoints","name":"inference api with openai-compatible endpoints","description":"Provides an HTTP API compatible with OpenAI's chat completion and text completion endpoints, allowing drop-in replacement of OpenAI with local ExLlamaV2 inference. The API handles request parsing, model loading, inference execution, and response formatting, supporting streaming responses and standard sampling parameters.","intents":["Replace OpenAI API calls with local inference without changing client code","Build inference servers compatible with existing OpenAI client libraries","Deploy local models with the same API surface as commercial LLM services"],"best_for":["Developers migrating from OpenAI to local inference","Teams building inference servers with OpenAI-compatible APIs","Builders prototyping with local models before deploying to cloud services"],"limitations":["API compatibility is partial; some OpenAI features (e.g., function calling, vision) are not supported","Response format may differ slightly from OpenAI (e.g., token counts, model names)","No authentication or rate limiting; requires external API gateway for production use","Streaming responses may have higher latency than OpenAI due to local GPU constraints"],"requires":["NVIDIA GPU with sufficient VRAM for model","Python 3.8+","FastAPI or other HTTP framework"],"input_types":["HTTP POST requests with JSON body (chat completion or text completion format)","Optional streaming parameter (stream=true for streaming responses)"],"output_types":["JSON response (OpenAI-compatible format)","Streaming responses (Server-Sent Events format)"],"categories":["tool-use-integration","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_13","uri":"capability://code.generation.editing.context.window.extension.with.position.interpolation.and.rope.scaling","name":"context window extension with position interpolation and rope scaling","description":"Extends the context window of models beyond their training length using position interpolation (PI) or Rotary Position Embedding (RoPE) scaling. These techniques adjust positional encodings to accommodate longer sequences without retraining, allowing inference on sequences longer than the model's original training context.","intents":["Process longer documents (>4K tokens) with models trained on shorter contexts (e.g., Llama 2 with 4K context extended to 8K)","Avoid retraining models when longer context is needed","Maintain model quality while extending context window"],"best_for":["Developers working with long-context tasks (document QA, summarization, code review)","Teams extending pre-trained models without full retraining","Builders optimizing for cost-effective long-context inference"],"limitations":["Quality degrades gracefully but noticeably beyond 1.5-2x the training context length; 4x extension may lose significant capability","Position interpolation and RoPE scaling are heuristics; no guarantee of correctness on out-of-distribution lengths","Requires model architecture support (standard transformer with RoPE or absolute position embeddings); not compatible with all models","No support for sparse or hierarchical attention patterns; full attention is still computed on extended context"],"requires":["Model with RoPE or absolute position embeddings (most modern LLMs)","Python 3.8+"],"input_types":["Model weights","Context extension factor (e.g., 2.0 for 2x extension)","Position interpolation or RoPE scaling method"],"output_types":["Model with extended context window","Inference on longer sequences"],"categories":["code-generation-editing","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_2","uri":"capability://code.generation.editing.flash.attention.2.integration.for.sub.quadratic.attention.computation","name":"flash attention 2 integration for sub-quadratic attention computation","description":"Integrates Flash Attention 2 kernels to compute self-attention in O(N) memory and reduced FLOPs by fusing the attention computation (QK^T, softmax, attention dropout, value multiplication) into a single GPU kernel that operates on blocks of the query/key/value matrices. This avoids materializing the full NxN attention matrix in memory, enabling longer context windows and faster inference on the same hardware.","intents":["Process longer input sequences (8K+ tokens) without running out of VRAM","Reduce attention computation latency by 2-3x through kernel fusion","Enable real-time inference on long-context tasks (document QA, summarization)"],"best_for":["Developers working with long-context models (Llama 2 Long, MPT-30B-Instruct, etc.)","Teams building RAG systems that require processing large document chunks","Builders optimizing for latency-critical inference on consumer GPUs"],"limitations":["Flash Attention 2 requires NVIDIA GPUs with Compute Capability 8.0+ (A100, RTX 3090, RTX 4090); older GPUs fall back to standard attention","Dropout during inference is disabled in Flash Attention 2 (only applies during training)","Numerical precision differs slightly from standard attention due to block-wise softmax; may cause minor output divergence","Not compatible with some custom attention patterns (e.g., sparse attention, grouped query attention variants)"],"requires":["NVIDIA GPU with Compute Capability 8.0+ (Ampere or newer: A100, RTX 3090, RTX 4090, H100)","CUDA 11.8+","Model with standard multi-head self-attention (no custom attention kernels)"],"input_types":["Query, Key, Value tensors (float16 or bfloat16)","Attention mask (optional, for causal or custom masking)","Sequence length metadata"],"output_types":["Attention output tensor (same shape as query input)","Attention weights (optional, for interpretability; requires separate computation)"],"categories":["code-generation-editing","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_3","uri":"capability://automation.workflow.dynamic.batching.with.automatic.request.scheduling.and.padding","name":"dynamic batching with automatic request scheduling and padding","description":"Implements a request queue and scheduler that batches multiple inference requests of varying lengths into a single GPU batch, automatically padding shorter sequences and scheduling requests to maximize GPU utilization. The scheduler uses a token-budget approach where it accumulates requests until adding another would exceed a configurable token limit, then executes the batch and immediately begins accumulating the next batch.","intents":["Serve multiple concurrent inference requests without blocking individual clients","Maximize GPU throughput by batching requests of different lengths efficiently","Reduce per-request latency by amortizing GPU kernel launch overhead across multiple requests"],"best_for":["Teams building inference servers (vLLM-style deployments) on consumer GPUs","Developers optimizing throughput for batch inference workloads","Builders implementing multi-user inference APIs with latency SLAs"],"limitations":["Padding shorter sequences to match the longest sequence in a batch increases computation; worst-case overhead is ~50% if batch contains one very long sequence and many short ones","Token-budget scheduling adds ~10-50ms latency per batch due to request accumulation; not suitable for ultra-low-latency (<10ms) applications","No support for priority queuing or SLA-aware scheduling; all requests are treated equally","Requires manual tuning of batch token budget; no adaptive tuning based on GPU memory availability"],"requires":["NVIDIA GPU with sufficient VRAM for largest expected batch","Python 3.8+","Multi-threaded or async request handler (e.g., FastAPI, asyncio)"],"input_types":["Multiple tokenized input sequences (variable length)","Optional per-request sampling parameters (temperature, top_p, etc.)"],"output_types":["Batched token logits or sampled tokens","Per-request output sequences"],"categories":["automation-workflow","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_4","uri":"capability://automation.workflow.speculative.decoding.with.draft.model.acceleration","name":"speculative decoding with draft model acceleration","description":"Implements speculative decoding where a smaller, faster draft model generates candidate tokens, and the main model validates them in parallel. If the draft model's predictions match the main model's top-1 choice, multiple tokens are accepted in a single forward pass; otherwise, the main model's prediction is used. This reduces the number of main model forward passes required to generate a sequence, achieving 1.5-2x speedup with minimal quality loss.","intents":["Accelerate inference on large models by 1.5-2x using a smaller draft model","Reduce latency for token generation without sacrificing output quality","Improve throughput on memory-bound inference workloads"],"best_for":["Teams running large models (70B+) where inference latency is a bottleneck","Developers building real-time chat or code generation interfaces","Builders optimizing for cost-per-token in inference services"],"limitations":["Requires a smaller draft model (typically 1/4 to 1/2 the size of the main model) to be loaded in VRAM simultaneously; total memory usage increases by 25-50%","Speedup depends on draft model quality; poor draft models may reject most predictions, reducing speedup to <1.2x","Adds complexity to deployment (two models to manage, version compatibility, etc.)","Not effective for very short sequences (<10 tokens) because overhead of draft model exceeds savings"],"requires":["NVIDIA GPU with sufficient VRAM for both main and draft models","Smaller draft model compatible with the main model's tokenizer","Python 3.8+"],"input_types":["Tokenized input sequence (integer token IDs)","Main model weights","Draft model weights","Sampling parameters (temperature, top_p, etc.)"],"output_types":["Generated token sequence (same quality as main model alone)","Acceptance rate metrics (for monitoring draft model quality)"],"categories":["automation-workflow","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_5","uri":"capability://code.generation.editing.lora.adapter.loading.and.inference.with.weight.merging","name":"lora adapter loading and inference with weight merging","description":"Loads Low-Rank Adaptation (LoRA) adapter weights and applies them to the base model during inference by computing the low-rank update (LoRA_A @ LoRA_B) and adding it to the original weight matrices. Supports multiple LoRA adapters with weighted combination, allowing fine-tuned behavior without modifying the base model weights or requiring full model retraining.","intents":["Apply task-specific fine-tuning (e.g., code generation, summarization) without loading separate model copies","Switch between multiple fine-tuned variants (e.g., different instruction styles) at inference time","Reduce storage overhead by storing only LoRA weights (~1-5% of base model size) instead of full fine-tuned models"],"best_for":["Teams deploying multiple task-specific variants of the same base model","Developers fine-tuning models for specific domains without full retraining","Builders optimizing storage and memory for multi-tenant inference services"],"limitations":["LoRA adapter application adds ~5-10% inference latency because low-rank updates must be computed and added to weights","LoRA rank is fixed at adapter creation time; cannot adjust rank at inference without retraining","Multiple LoRA adapters cannot be combined with arbitrary weights; only linear combinations are supported","LoRA adapters are task-specific; a LoRA trained for code generation may not work well for summarization"],"requires":["Base model weights (quantized or full-precision)","LoRA adapter weights (.safetensors or .bin format)","LoRA rank and target modules metadata (typically in adapter_config.json)","Python 3.8+"],"input_types":["Base model weights","LoRA adapter weights (low-rank matrices A and B)","Adapter configuration (rank, target modules, scaling factor)","Tokenized input sequence"],"output_types":["Token logits (with LoRA updates applied)","Generated token sequence"],"categories":["code-generation-editing","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_6","uri":"capability://text.generation.language.streaming.token.generation.with.configurable.sampling.strategies","name":"streaming token generation with configurable sampling strategies","description":"Generates tokens one at a time (or in small groups with speculative decoding) and streams them to the caller, supporting multiple sampling strategies including temperature scaling, top-k filtering, top-p (nucleus) sampling, and repetition penalty. The framework maintains generation state (KV cache, sequence length) across token steps, allowing the caller to interrupt or modify sampling parameters mid-generation.","intents":["Stream generated tokens to a client in real-time (e.g., for chat interfaces)","Implement custom sampling logic (e.g., constrained decoding, beam search) by intercepting token logits","Control generation behavior (temperature, top_p, repetition penalty) per-request without reloading the model"],"best_for":["Developers building chat interfaces or real-time code generation tools","Teams implementing streaming APIs (e.g., OpenAI-compatible endpoints)","Builders experimenting with custom sampling strategies and decoding algorithms"],"limitations":["Streaming adds ~1-5ms per token due to Python-GPU synchronization overhead; not suitable for ultra-low-latency applications","KV cache grows linearly with sequence length; very long sequences (>8K tokens) may exhaust VRAM","Sampling parameters (temperature, top_p) cannot be changed mid-generation without restarting; must commit to parameters at generation start","No built-in support for constrained decoding (e.g., JSON schema validation) or beam search; requires custom implementation"],"requires":["NVIDIA GPU with sufficient VRAM for model + KV cache","Python 3.8+","Async or threaded caller to handle streaming without blocking"],"input_types":["Tokenized input prompt (integer token IDs)","Sampling parameters (temperature, top_k, top_p, repetition_penalty, max_new_tokens)","Optional stopping criteria (e.g., stop tokens, max length)"],"output_types":["Token stream (one token at a time)","Optional token probabilities or logits (for analysis)"],"categories":["text-generation-language","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_7","uri":"capability://memory.knowledge.kv.cache.management.with.automatic.eviction.and.reuse","name":"kv cache management with automatic eviction and reuse","description":"Manages the Key-Value (KV) cache that stores intermediate attention computations across token generation steps. The framework automatically allocates cache space, reuses cache entries for identical prefixes (e.g., in batch processing), and evicts old cache entries when VRAM is exhausted. This reduces memory overhead and enables longer sequences without running out of VRAM.","intents":["Generate longer sequences without running out of VRAM due to KV cache growth","Reuse KV cache across multiple requests with the same prompt prefix (e.g., system prompt)","Understand and optimize KV cache memory usage for a given model and batch size"],"best_for":["Teams generating very long sequences (>4K tokens) on consumer GPUs","Developers building multi-turn chat systems where prefixes are reused","Builders optimizing memory usage for high-throughput inference servers"],"limitations":["KV cache grows linearly with sequence length; a 70B model with 8K context requires ~100GB of cache (larger than the model itself)","Cache reuse requires exact prefix matching; even slight differences in prompts prevent reuse","Automatic eviction may discard useful cache entries if VRAM is exhausted, requiring regeneration of tokens","No support for sparse or hierarchical cache structures; all cache entries are stored densely in VRAM"],"requires":["NVIDIA GPU with sufficient VRAM for model + KV cache","Python 3.8+"],"input_types":["Sequence length and batch size metadata","Model configuration (hidden size, num_layers, num_heads)","Optional cache reuse hints (e.g., shared prefix length)"],"output_types":["KV cache tensors (stored in VRAM)","Cache statistics (memory usage, hit rate, eviction count)"],"categories":["memory-knowledge","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_8","uri":"capability://automation.workflow.multi.gpu.inference.with.tensor.parallelism","name":"multi-gpu inference with tensor parallelism","description":"Distributes model weights and computation across multiple GPUs using tensor parallelism, where each GPU holds a partition of the weight matrices and performs partial matrix multiplications. The framework automatically splits tensors along the appropriate dimensions, synchronizes partial results via all-reduce operations, and overlaps communication with computation to minimize latency.","intents":["Run models larger than a single GPU's VRAM (e.g., 70B+ models) by distributing across multiple GPUs","Achieve near-linear speedup by parallelizing computation across multiple GPUs","Reduce per-GPU memory usage by partitioning weights across the GPU cluster"],"best_for":["Teams with multi-GPU setups (2+ GPUs) running large models","Developers optimizing throughput for high-concurrency inference servers","Builders deploying models that exceed single-GPU VRAM limits"],"limitations":["Tensor parallelism requires high-bandwidth GPU interconnect (NVLink, PCIe 4.0+); slow interconnects (PCIe 3.0) may reduce speedup to <1.5x","All-reduce communication adds ~5-20% overhead per layer; speedup is sublinear with number of GPUs","Requires model architecture to support tensor parallelism (standard transformer models work; custom architectures may not)","Setup complexity increases significantly; requires careful tuning of tensor parallel degree and communication patterns"],"requires":["2+ NVIDIA GPUs with CUDA Compute Capability 6.0+","High-bandwidth GPU interconnect (NVLink preferred; PCIe 4.0 minimum)","CUDA 11.8+, NCCL 2.0+","Python 3.8+"],"input_types":["Model weights (partitioned across GPUs)","Tokenized input sequence","Tensor parallel degree (number of GPUs to use)"],"output_types":["Token logits (computed across all GPUs)","Generated token sequence"],"categories":["automation-workflow","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__cap_9","uri":"capability://code.generation.editing.quantization.aware.fine.tuning.with.gradient.computation.on.quantized.weights","name":"quantization-aware fine-tuning with gradient computation on quantized weights","description":"Supports fine-tuning of quantized models by computing gradients through quantized weight matrices using straight-through estimators (STE) or other gradient approximations. The framework keeps weights quantized during forward and backward passes, avoiding full-precision weight materialization and enabling efficient fine-tuning on consumer GPUs.","intents":["Fine-tune quantized models (EXL2, GPTQ) on consumer GPUs without dequantizing","Adapt pre-quantized models to new tasks without full retraining","Reduce memory overhead of fine-tuning by keeping weights quantized throughout training"],"best_for":["Researchers and developers fine-tuning quantized models on limited hardware","Teams adapting pre-quantized models to domain-specific tasks","Builders optimizing for cost-effective model adaptation"],"limitations":["Gradient computation through quantized weights is approximate; convergence may be slower than full-precision fine-tuning","Straight-through estimators (STE) ignore quantization in backward pass, leading to gradient mismatch","Fine-tuning may degrade quantization quality if not carefully regularized; requires careful learning rate tuning","No support for mixed-precision fine-tuning (e.g., quantized weights + full-precision gradients); all computations are quantized"],"requires":["Quantized model weights (EXL2 or GPTQ format)","Training data (tokenized sequences)","NVIDIA GPU with sufficient VRAM for model + optimizer state","Python 3.8+"],"input_types":["Quantized model weights","Training data (tokenized sequences)","Learning rate, batch size, and other training hyperparameters"],"output_types":["Fine-tuned quantized model weights","Training metrics (loss, perplexity, etc.)"],"categories":["code-generation-editing","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"exllamav2__headline","uri":"capability://model.training.optimized.inference.library.for.quantized.llms.on.consumer.gpus","name":"optimized inference library for quantized llms on consumer gpus","description":"ExLlamaV2 is an optimized inference library designed for running quantized large language models on consumer GPUs, offering features like flash attention and dynamic batching for efficient local inference.","intents":["best optimized inference library for LLMs","inference library for quantized models on consumer GPUs","how to run quantized LLMs locally","best tools for efficient LLM inference","ExLlamaV2 features and benefits"],"best_for":["developers seeking efficient LLM inference","users with consumer GPUs"],"limitations":["requires compatible GPU hardware"],"requires":["quantized LLM models"],"input_types":["quantized LLMs"],"output_types":["inference results"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["NVIDIA GPU with CUDA Compute Capability 6.0+ (GTX 1060 or newer)","CUDA 11.8+ and cuDNN 8.0+","Pre-quantized EXL2 model files (e.g., from HuggingFace hub)","Python 3.8+","NVIDIA GPU with CUDA Compute Capability 6.0+","Pre-quantized GPTQ model files","NVIDIA GPU with sufficient VRAM for largest batch","Full-precision model weights (.safetensors or .bin format)","Calibration dataset (representative samples for sensitivity analysis)","NVIDIA GPU with sufficient VRAM for full-precision model"],"failure_modes":["EXL2 quantization is lossy; quality degrades with aggressive bit reduction (2-3 bits) compared to FP16 baseline","Requires pre-quantized EXL2 model files; cannot quantize arbitrary GGUF or safetensors models in-place","Dynamic bit allocation adds ~5-10% inference overhead vs static quantization due to per-token routing logic","No support for quantizing models larger than available VRAM during inference","GPTQ quality is lower than EXL2 because it uses uniform bit widths per group rather than dynamic allocation","Group size is fixed at quantization time (typically 128); cannot adjust granularity at inference","Requires exact group size match between quantized model and inference kernel; mismatches cause silent numerical errors","No built-in support for mixed-precision groups (e.g., 4-bit + 8-bit in same model)","Padding shorter sequences to match the longest sequence increases computation; worst-case overhead is ~50% if batch contains one very long sequence","Attention masking adds ~5-10% overhead due to mask generation and application","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":"2026-06-17T09:51:04.691Z","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=exllamav2","compare_url":"https://unfragile.ai/compare?artifact=exllamav2"}},"signature":"veD0p+83O7T9etR0136AHYoyKdStKkjW94Z2JqQIvrPaFqfvAncwFUWvDAFt+MeF89nRO5fd0QHURXnICX1WBg==","signedAt":"2026-06-20T10:11:13.019Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/exllamav2","artifact":"https://unfragile.ai/exllamav2","verify":"https://unfragile.ai/api/v1/verify?slug=exllamav2","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"}}