{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-exllamav2","slug":"pypi-exllamav2","name":"exllamav2","type":"repo","url":"https://github.com/turboderp/exllamav2","page_url":"https://unfragile.ai/pypi-exllamav2","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-exllamav2__cap_0","uri":"capability://code.generation.editing.gpu.accelerated.llm.inference.with.4.bit.quantization","name":"gpu-accelerated llm inference with 4-bit quantization","description":"Implements custom CUDA kernels for efficient inference of large language models on consumer GPUs using 4-bit quantization, enabling models like Llama 70B to run on single 24GB GPUs. Uses fused attention mechanisms and optimized memory layouts to reduce bandwidth bottlenecks, with dynamic batch sizing and token-by-token generation for low-latency streaming responses.","intents":["Run large open-source LLMs locally without cloud API costs","Deploy quantized models on consumer hardware with minimal latency","Stream token generation for real-time chat applications","Maximize throughput for batch inference on limited VRAM"],"best_for":["Solo developers building local LLM applications","Teams deploying inference servers on edge hardware","Researchers experimenting with quantization techniques","Cost-conscious builders avoiding cloud LLM APIs"],"limitations":["CUDA-only — no CPU fallback or AMD GPU support (requires NVIDIA hardware)","4-bit quantization introduces ~2-5% accuracy degradation vs FP16 depending on model","Inference speed degrades significantly with context lengths >4K tokens due to KV cache memory pressure","Requires model conversion to ExLlama format (~30 min for 70B model), not plug-and-play with standard GGUF"],"requires":["NVIDIA GPU with compute capability 7.0+ (RTX 2060 or newer)","CUDA 12.0+","Python 3.8+","8GB+ VRAM minimum (24GB+ recommended for 70B models)"],"input_types":["text prompts","quantized model weights (ExLlama format)","sampling parameters (temperature, top_p, top_k)"],"output_types":["text tokens (streaming or batched)","logits for custom decoding","token probabilities"],"categories":["code-generation-editing","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_1","uri":"capability://automation.workflow.dynamic.batch.inference.with.variable.sequence.lengths","name":"dynamic batch inference with variable sequence lengths","description":"Manages heterogeneous batch processing where requests have different prompt/completion lengths, using a paged attention mechanism to avoid padding waste. Dynamically schedules GPU compute based on available VRAM and request queue, reordering batches to maximize occupancy without head-of-line blocking.","intents":["Process multiple inference requests simultaneously without padding overhead","Maximize GPU utilization when handling mixed-length prompts","Implement request queuing for production inference servers","Balance latency and throughput for concurrent users"],"best_for":["Production inference servers handling variable-length requests","Multi-user chat applications with concurrent sessions","Batch processing pipelines with heterogeneous inputs","Real-time systems requiring predictable latency bounds"],"limitations":["Scheduling overhead adds ~50-100ms per batch decision cycle","No support for dynamic batching across different model instances","Requires pre-allocation of maximum batch size at startup — cannot scale beyond initial configuration","Paged attention adds ~5-10% memory overhead vs contiguous KV cache"],"requires":["NVIDIA GPU with sufficient VRAM for target batch size","Python 3.8+","ExLlama model in quantized format"],"input_types":["list of text prompts with variable lengths","batch size configuration","sampling parameters per request"],"output_types":["batched token predictions","per-request completion status","timing metrics (queue wait, compute time)"],"categories":["automation-workflow","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_10","uri":"capability://planning.reasoning.speculative.decoding.with.draft.model.acceleration","name":"speculative decoding with draft model acceleration","description":"Accelerates inference using speculative decoding with a smaller draft model that generates multiple token candidates, which are verified by the main model in parallel. Implements efficient batch verification with early exit when draft predictions diverge, reducing main model inference calls by 30-50% on typical workloads.","intents":["Reduce latency for token generation by 30-50% using draft model acceleration","Implement speculative decoding without modifying model architecture","Balance draft model size vs verification overhead for optimal speedup","Deploy on systems with heterogeneous GPU resources"],"best_for":["Production systems requiring low-latency inference","Multi-GPU systems with heterogeneous compute (e.g., one large, one small GPU)","Applications where 30-50% latency reduction justifies draft model overhead","Researchers studying speculative decoding techniques"],"limitations":["Requires training or obtaining a smaller draft model — not automatic","Draft model quality significantly impacts speedup — poor drafts provide minimal benefit","Verification overhead can exceed draft computation for very small draft models","No support for adaptive draft model selection based on prompt characteristics"],"requires":["Main ExLlama quantized model","Smaller draft model (same architecture, 10-30% of main model size)","Python 3.8+","Sufficient VRAM for both models"],"input_types":["main model","draft model","text prompts","draft model configuration (num candidates, max draft tokens)"],"output_types":["text completions","speculative decoding metrics (draft acceptance rate, speedup factor)","per-token timing breakdown"],"categories":["planning-reasoning","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_2","uri":"capability://code.generation.editing.multi.lora.adapter.composition.and.switching","name":"multi-lora adapter composition and switching","description":"Loads and composes multiple Low-Rank Adaptation (LoRA) modules on top of a base quantized model, enabling dynamic switching between task-specific adapters without reloading the base weights. 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Implements efficient logit filtering and probability normalization in CUDA to avoid CPU bottlenecks, with support for repetition penalties and frequency-based constraints.","intents":["Stream LLM responses token-by-token for real-time chat UIs","Implement custom sampling strategies for specialized generation tasks","Control generation diversity and determinism per-request","Apply repetition penalties and content constraints during generation"],"best_for":["Chat applications requiring low-latency streaming responses","Systems needing fine-grained control over generation behavior","Research projects experimenting with sampling strategies","Production systems requiring deterministic or constrained generation"],"limitations":["Streaming adds ~5-15ms per token overhead vs batch generation due to GPU kernel launch costs","Custom sampling strategies cannot be dynamically changed mid-generation without recompilation","Repetition penalties are approximate — use heuristic-based filtering rather than exact constraint satisfaction","No support for guided generation or constrained decoding (e.g., JSON schema enforcement)"],"requires":["ExLlama quantized model","Python 3.8+","NVIDIA GPU for CUDA-accelerated sampling"],"input_types":["prompt text","sampling parameters (temperature, top_k, top_p, min_p, typical_p)","repetition penalty values","token frequency tracking state"],"output_types":["individual tokens (streaming)","token probabilities","generation metadata (stop reason, token count)"],"categories":["text-generation-language","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_4","uri":"capability://memory.knowledge.context.window.extension.via.rope.interpolation","name":"context window extension via rope interpolation","description":"Extends model context windows beyond training length using Rotary Position Embedding (RoPE) interpolation, dynamically adjusting position encoding frequencies to fit longer sequences into the same embedding space. Implements linear and NTK-aware interpolation strategies to maintain coherence at extended lengths, with configurable interpolation factors per model.","intents":["Process documents longer than model training context (e.g., 32K+ tokens with 4K-trained models)","Reduce context truncation in long-form document analysis","Extend context windows without retraining or fine-tuning","Experiment with position encoding interpolation techniques"],"best_for":["Document analysis systems handling long texts","Researchers studying context extension techniques","Applications requiring variable context windows","Teams avoiding fine-tuning costs for context extension"],"limitations":["Interpolation introduces ~5-15% accuracy degradation at extended lengths depending on interpolation method","Performance degrades significantly beyond 2x training context length","No theoretical guarantee of coherence — empirically tested but model-dependent","Requires manual tuning of interpolation factor per model architecture"],"requires":["ExLlama quantized model with RoPE position encoding","Python 3.8+","Knowledge of model training context length"],"input_types":["model configuration","interpolation strategy (linear, NTK-aware)","target context length","text prompts up to extended length"],"output_types":["model with extended context window","inference results on long sequences","interpolation metadata"],"categories":["memory-knowledge","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_5","uri":"capability://data.processing.analysis.quantization.aware.model.conversion.and.optimization","name":"quantization-aware model conversion and optimization","description":"Converts standard HuggingFace models to ExLlama's optimized quantized format using 4-bit quantization with per-channel scaling, applying layer-wise calibration on representative data to minimize quantization error. Includes automatic layer fusion (e.g., combining linear layers with activation functions) and weight reordering for cache-optimal GPU memory access patterns.","intents":["Convert open-source models to efficient quantized format for local deployment","Optimize model memory layout for GPU inference performance","Reduce model size by 75% through 4-bit quantization with minimal accuracy loss","Automate quantization pipeline without manual calibration"],"best_for":["Teams deploying models on consumer GPUs","Researchers benchmarking quantization techniques","Developers building local-first LLM applications","Systems requiring model size reduction for storage/distribution"],"limitations":["Conversion process is one-way — cannot easily revert to original precision","Calibration data selection significantly impacts final accuracy — requires domain-specific tuning","Conversion time scales with model size (~30 min for 70B on CPU, longer for larger models)","No support for mixed-precision quantization (e.g., 8-bit for attention, 4-bit for FFN)"],"requires":["HuggingFace model in standard format (safetensors or PyTorch)","Python 3.8+","8GB+ RAM for conversion process","Representative calibration dataset (optional but recommended)"],"input_types":["HuggingFace model weights","model configuration (config.json)","calibration data (text samples)","quantization parameters (group size, bits)"],"output_types":["ExLlama quantized model","quantization statistics (per-layer error metrics)","optimized model metadata"],"categories":["data-processing-analysis","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_6","uri":"capability://automation.workflow.multi.gpu.distributed.inference.with.tensor.parallelism","name":"multi-gpu distributed inference with tensor parallelism","description":"Distributes model inference across multiple GPUs using tensor parallelism, splitting weight matrices horizontally across devices and coordinating all-reduce operations for attention and FFN layers. Implements efficient GPU-to-GPU communication via NVLink or PCIe, with automatic load balancing and pipeline scheduling to minimize synchronization overhead.","intents":["Run models larger than single GPU VRAM on multi-GPU systems","Increase throughput by parallelizing inference across GPUs","Deploy models on multi-GPU servers or clusters","Reduce per-GPU memory footprint for larger models"],"best_for":["Production inference servers with 2+ GPUs","Teams deploying 70B+ models on multi-GPU hardware","High-throughput batch processing systems","Researchers studying distributed inference optimization"],"limitations":["Communication overhead scales with number of GPUs — diminishing returns beyond 4 GPUs for single-model inference","Requires NVLink or high-bandwidth PCIe for acceptable performance — slow on older interconnects","All-reduce synchronization creates pipeline bubbles — cannot fully hide communication latency","No support for pipeline parallelism or expert parallelism (MoE models)"],"requires":["2+ NVIDIA GPUs with NVLink or high-bandwidth PCIe","CUDA 12.0+","Python 3.8+","NCCL 2.14+ for efficient collective operations"],"input_types":["ExLlama quantized model","number of GPUs to parallelize across","text prompts","sampling parameters"],"output_types":["text tokens (from primary GPU)","per-GPU timing metrics","communication statistics"],"categories":["automation-workflow","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_7","uri":"capability://memory.knowledge.prompt.caching.and.kv.cache.reuse.across.requests","name":"prompt caching and kv cache reuse across requests","description":"Caches computed key-value (KV) cache for prompt prefixes across multiple requests, enabling instant reuse of expensive attention computations when requests share common context. Implements a cache key based on token sequence hash with LRU eviction, supporting both exact-match and approximate-match cache hits for flexible prompt variations.","intents":["Reduce latency for requests with shared system prompts or context","Implement few-shot learning without recomputing prompt embeddings","Build RAG systems where document context is reused across queries","Optimize multi-turn conversations by caching conversation history"],"best_for":["Chat applications with consistent system prompts","RAG systems processing multiple queries over same documents","Few-shot learning scenarios with fixed examples","Multi-turn conversation systems"],"limitations":["Cache invalidation is manual — no automatic detection of semantic equivalence","Approximate matching adds ~5-10% overhead vs exact matching","Cache memory overhead scales with number of unique prefixes — requires careful eviction policy tuning","No support for dynamic cache updates — cached KV cannot be modified, only replaced"],"requires":["ExLlama quantized model","Python 3.8+","Sufficient GPU VRAM for cache storage (typically 10-30% of model size)"],"input_types":["prompt text","cache key (token sequence hash or custom identifier)","cache policy (LRU, LFU, TTL)","max cache size in tokens"],"output_types":["cached KV tensors","cache hit/miss statistics","inference results with cache metadata"],"categories":["memory-knowledge","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_8","uri":"capability://tool.use.integration.python.api.with.async.streaming.support.for.integration","name":"python api with async/streaming support for integration","description":"Provides a high-level Python API wrapping the CUDA inference engine with async/await support for non-blocking inference, streaming token callbacks, and batch request handling. Implements context managers for resource cleanup, type hints for IDE autocomplete, and integration hooks for custom sampling or post-processing logic.","intents":["Integrate ExLlama into Python applications with minimal boilerplate","Build async inference servers without blocking on model computation","Stream tokens to client applications in real-time","Extend inference pipeline with custom processing steps"],"best_for":["Python developers building LLM applications","Teams integrating inference into existing Python codebases","Async web frameworks (FastAPI, Starlette) requiring non-blocking inference","Researchers prototyping LLM systems quickly"],"limitations":["Async overhead adds ~5-10ms per request due to Python event loop scheduling","Type hints are incomplete for complex nested structures — some IDE autocomplete gaps","Custom sampling callbacks introduce ~2-5% performance overhead vs built-in sampling","No support for distributed inference across multiple Python processes (GIL limitation)"],"requires":["Python 3.8+","ExLlama compiled CUDA extensions","asyncio or compatible async runtime"],"input_types":["text prompts","sampling parameters","model configuration","custom callback functions"],"output_types":["text completions","token streams (via callbacks)","structured inference metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-exllamav2__cap_9","uri":"capability://automation.workflow.benchmark.and.profiling.tools.for.inference.optimization","name":"benchmark and profiling tools for inference optimization","description":"Includes built-in profiling utilities to measure token generation speed, memory usage, and GPU utilization across different batch sizes, sequence lengths, and quantization settings. Generates detailed performance reports with bottleneck identification (compute-bound vs memory-bound) and recommendations for optimization (batch size tuning, context length reduction, etc.).","intents":["Measure inference performance on target hardware before deployment","Identify performance bottlenecks (compute vs memory bandwidth)","Tune batch size and context length for optimal throughput","Compare performance across different models and quantization settings"],"best_for":["Teams optimizing inference performance for production","Researchers benchmarking quantization techniques","Developers tuning model deployment configurations","Systems engineers capacity planning for inference infrastructure"],"limitations":["Benchmarks are synthetic — real-world performance depends on prompt/completion length distribution","Profiling overhead adds ~5-10% to measured latency","No support for profiling multi-GPU distributed inference","Memory measurements are approximate — actual peak usage may vary with CUDA memory fragmentation"],"requires":["ExLlama quantized model","Python 3.8+","NVIDIA GPU with profiling support"],"input_types":["model path","benchmark parameters (batch sizes, sequence lengths, num iterations)","sampling configuration"],"output_types":["performance metrics (tokens/sec, latency, memory usage)","bottleneck analysis","optimization recommendations","CSV/JSON reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["NVIDIA GPU with compute capability 7.0+ (RTX 2060 or newer)","CUDA 12.0+","Python 3.8+","8GB+ VRAM minimum (24GB+ recommended for 70B models)","NVIDIA GPU with sufficient VRAM for target batch size","ExLlama model in quantized format","Main ExLlama quantized model","Smaller draft model (same architecture, 10-30% of main model size)","Sufficient VRAM for both models","Base model in ExLlama quantized format"],"failure_modes":["CUDA-only — no CPU fallback or AMD GPU support (requires NVIDIA hardware)","4-bit quantization introduces ~2-5% accuracy degradation vs FP16 depending on model","Inference speed degrades significantly with context lengths >4K tokens due to KV cache memory pressure","Requires model conversion to ExLlama format (~30 min for 70B model), not plug-and-play with standard GGUF","Scheduling overhead adds ~50-100ms per batch decision cycle","No support for dynamic batching across different model instances","Requires pre-allocation of maximum batch size at startup — cannot scale beyond initial configuration","Paged attention adds ~5-10% memory overhead vs contiguous KV cache","Requires training or obtaining a smaller draft model — not automatic","Draft model quality significantly impacts speedup — poor drafts provide minimal benefit","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:19.404Z","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=pypi-exllamav2","compare_url":"https://unfragile.ai/compare?artifact=pypi-exllamav2"}},"signature":"CfxKHkGkrMr64cfaaURsIMpMRAKzPg/14ijkyckdyF0C1nQITDDzBGWFPt1M0iLaXalkNqgAncIwHt4TewqiBw==","signedAt":"2026-06-20T03:03:51.415Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-exllamav2","artifact":"https://unfragile.ai/pypi-exllamav2","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}