Open-source local inference just got real. A single GPU can host dozens of big models with smart caching, and llama.cpp speedups plus desktop tooling are making this practical for hobbyists and teams [1][2].
Single‑GPU inference, open tools, big ideas - flashtensors shows you can serve 100 large AI models on one GPU with low TTFT impact, loading models from SSD to VRAM up to ten times faster than some rivals. It plays nicely with vLLM and transformers [1].
Speed and efficiency on the prompt - proxycache sits in front of llama.cpp to manage slots, reuse cached contexts, and restore saved caches from disk. It’s OpenAI‑compatible and trims latency on long prompts by keeping hot slots alive when it can [2].
Local tooling for desktop comfort - Goose Desktop and Goose CLI are making local LLMs easier to run with tool‑calling workflows; users report mixed success depending on the model and setup (e.g., Ollama) [3]. - LlamaBarn brings a macOS menu bar touchpoint to running local LLMs, simplifying day‑to‑day experiments [4].
Hardware reality: VRAM and budgets matter - VRAM options around GLM 4.5V show you can push 32GB‑class GPUs and even work with 16GB cards when models fit the gguf math, but expect 70GB‑scale requirements for some 30B families [6]. - A budget‑minded build using three GTX-1070 GPUs on DDR4 can deliver solid 30B model throughput, underscoring that GPU choice often matters more than CPU in open stacks [7].
Closing thought: the open‑source local stack—driven by Goose, LlamaBarn, proxycache, and flashtensors—is maturing fast. Watch VRAM economics and shopper‑friendly GPUs as this space tightens [6][7].
References
Show HN: Serve 100 Large AI models on a single GPU with low impact to TTFT
Announcement of a GPU-based engine loading large models from SSD to VRAM, speeds up inference, compatible with vLLM/transformers, open source.
View sourceFaster Prompt Processing in llama.cpp: Smart Proxy + Slots + Restore
Smart proxy optimizes llama.cpp by managing slots, caching, and restore, enabling OpenAI-like chat while preserving hot caches for long prompts.
View sourceCodename Goose Desktop and Goose CLI with Ollama or other local inference
Trying Goose Desktop/CLI with Ollama for local models; discuss function/tool calling, context length, and model compatibility.
View sourceLlamaBarn – A macOS menu bar app for running local LLMs
LlamaBarn is a macOS menu bar app to run local LLMs, enabling quick access and management.
View sourceVRAM options for GLM 4.5V
Query about VRAM capacity; mentions two Mi50 32GB and P100 16GB; GLM 4.5 air baseline; gguf format ~70GB.
View sourceBudget system for 30B models revisited
Shows multiple 30B-class models on GTX-1070 GPUs; compares Vulkan vs CUDA backends; reports modest speeds and backends in LLAMA bench.
View source