5Hugging Face Blog·
Liquid AI releases LFM2.5-Encoders for fast long-context CPU inference
Liquid AI published a Hugging Face blog post introducing LFM2.5-Encoders, a new family of encoder models designed for fast long-context inference on CPU hardware. The release targets deployment scenarios where GPU resources are unavailable or cost-prohibitive, positioning these models as efficient alternatives for embedding and encoding workloads. This is notable as a continuation of Liquid AI's LFM (Liquid Foundation Model) architecture line, extending it to encoder-specific use cases.
Related guides (4)
Related events (8)
Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs
Hugging Face published a case study examining the inference performance of their Infinity product on modern CPUs, targeting millisecond-level latency for NLP model serving. The post explores CPU-based deployment as a cost-effective alternative to GPU inference for transformer models. This is relevant to the inference economics and enterprise deployment patterns threads, though the content is from early 2022.
Accelerating Hugging Face Transformers with AWS Inferentia2
Hugging Face published a blog post detailing how to accelerate Transformer model inference using AWS Inferentia2, Amazon's second-generation ML inference chip. The post covers integration patterns between the Hugging Face ecosystem and the Neuron SDK for deploying models on Inferentia2 hardware. This represents a practical guide for enterprise and cloud-based inference deployment using dedicated AI accelerators.
Latent Context Language Models (LCLMs) achieve competitive encoder-decoder KV cache compression at scale
Researchers introduce Latent Context Language Models (LCLMs), a family of encoder-decoder compressors that map long token sequences to shorter latent embeddings consumed by a decoder, targeting the KV cache memory bottleneck in long-context inference. The authors conduct architecture search and continually pre-train 0.6B-encoder/4B-decoder models on over 350B tokens at compression ratios of 1:4, 1:8, and 1:16. LCLMs improve the Pareto frontier across general-task performance, compression speed, and peak memory, and are demonstrated as efficient backbones for long-horizon agents that can skim compressed context and expand relevant segments on demand. The work closes a previously noted gap between encoder-decoder approaches and KV cache compression methods on the accuracy-efficiency frontier.
CPU Optimized Embeddings with Optimum Intel and fastRAG
Hugging Face and Intel demonstrate CPU-optimized embedding inference using Optimum Intel and fastRAG, targeting RAG pipeline acceleration without GPU hardware. The post covers quantization and optimization techniques that improve embedding throughput on Intel CPUs. This is relevant to inference economics and enterprise deployment patterns where GPU availability is constrained.
Featherless AI Joins Hugging Face Inference Providers
Hugging Face has added Featherless AI as a new inference provider in its Inference Providers ecosystem. Featherless AI specializes in serverless inference for open-weight models, expanding the range of third-party compute options available through the Hugging Face platform. This integration allows developers to route model inference requests to Featherless AI directly via the Hugging Face API and model hub.
Accelerate a World of LLMs on Hugging Face with NVIDIA NIM
NVIDIA NIM microservices are being integrated with Hugging Face to enable optimized inference deployment for a broad range of LLMs hosted on the Hub. The partnership allows developers to deploy Hugging Face models via NIM's containerized inference stack, leveraging NVIDIA's TensorRT-LLM and other optimizations. This expands the ecosystem of models accessible through NIM beyond NVIDIA's own catalog to the wider Hugging Face model repository.
Accelerating LLM Inference with TGI on Intel Gaudi
Hugging Face's Text Generation Inference (TGI) framework has added a backend for Intel Gaudi accelerators, enabling LLM inference on Intel's AI hardware. The integration allows users to deploy large language models on Gaudi hardware using TGI's serving infrastructure. This expands the hardware ecosystem for LLM inference beyond NVIDIA GPUs, offering an alternative accelerator option for enterprise deployments.
OpenAI and Broadcom unveil Jalapeño, a custom LLM inference chip
OpenAI and Broadcom have jointly announced Jalapeño, a custom silicon chip designed specifically for LLM inference workloads. The chip targets improvements in performance, efficiency, and scale across AI systems. This marks OpenAI's entry into custom inference silicon, reducing dependence on third-party GPU suppliers for inference at scale.