Tools tagged with "Speculative decoding"

Slotstream runs Qwen3.8-Flash-Next on Apple Silicon Macs that don't have enough RAM to hold the whole model. It's aimed at people with 16 to 64 GB of memory who want local chat, image questions or a model backend for coding agents. Most model weights stay on the SSD, while frequently used expert networks stay in memory. The full model remains available.

Pooled runs a single open model across browser tabs on laptops, desktops and phones, combining their memory when the model won't fit on one device. It's for people who want local AI chat or a coding assistant using hardware they already have. It's open source under the MIT license and requires no account or per-device installation.

vLLM is an open source engine for serving large language models on hardware you control. It suits developers and teams that need to handle many requests through an API while making efficient use of memory and compute. It's licensed under Apache 2.0 and can run with GPUs or on a CPU.

llama.cpp runs language models on your own hardware and can serve them from a machine you control. It’s an MIT-licensed, open source inference engine for people building local AI apps, running a private model server, or using a model directly from the command line. It supports vision-language models too.

SGLang is a self-hosted inference framework for teams that need to serve language and multimodal models on their own hardware. It runs on a single GPU or across distributed clusters and exposes an OpenAI-compatible API. The project is open source under the Apache 2.0 license.

Sonar is a self-hosted inference engine for developers and teams serving Hugging Face-compatible language and multimodal models on their own hardware. Based on vLLM, it adds model and quantization formats, sampling methods, and deployment features. It's open source under AGPL-3.0.

Intel IPEX-LLM is a library for developers running or fine-tuning models on Intel hardware. The project is archived and no longer maintained. Intel reports known security issues and no longer accepts patches or provides updates. The code is open source under Apache 2.0.

ExLlamaV2 is a local LLM inference library for developers and people hosting models on their own consumer GPUs. ExLlamaV2 is archived and no longer maintained; development continues in ExLlamaV3. The V2 library is free and open source under the MIT license, runs on Windows and Linux, and uses NVIDIA GPUs through CUDA. It supports multiple GPUs.

TabbyAPI is a self-hosted LLM API server built around ExLlamaV3, for people who want local model inference behind an OpenAI-compatible API. It's the official server for that backend. The project targets personal use and small groups, and its maintainers explicitly advise against using it for production workloads.

llama-cpp-python brings llama.cpp model inference into Python applications and exposes it through a self-hosted OpenAI-compatible server. It's for developers building local AI applications or connecting existing API clients to models on their own hardware. The package is open source under the MIT license.

TensorRT-LLM is a library for developers running LLMs on their own NVIDIA GPUs or self-hosted servers. It focuses on inference performance, with support for a single GPU, multiple GPUs, or deployments spread across several machines. Its PyTorch architecture lets teams adapt models and extend the runtime in Python.