A wave of guides, benchmarks and hardware releases shows LLMs running locally on Apple Silicon, RTX GPUs, edge SoCs and dedicated desktops. Market gray modifications and new on-device cores push VRAM, bandwidth and power envelopes for self-hosted inference.
A 96 GB VRAM RTX 5090 sold on Alibaba for under $4,000 shows growing gray-market demand for affordable on-premise inference. The Chinese mod triples the standard card's memory, lowering the barrier for running larger models locally. But without warra...
Qualcomm details the next-generation Oryon CPU, Adreno GPU, and Hexagon NPU. The move confirms that local inference becomes a primary design constraint for client chips and a strategic option for organizations evaluating self-hosted deployments and d...
A modder replaced a RTX 5090's 16-pin power connector with three 8-pin connectors, pushing the card to 900 watts and 3,400 MHz. The mod highlights the tension between peak power, power delivery reliability, and cooling, with implications for anyone u...
The new Apple A20 Pro SoC doubles the Neural Engine from 16 to 32 cores and, thanks to a 96-bit LPDDR5X bus on a 2nm process, reaches about 115 GB/s of memory bandwidth. A costly investment that signals Apple's strategic priority for local model exec...
System76 has introduced the Thelio Mira AI, a desktop designed for multi-GPU AI training, fine-tuning, inference, computer vision, and image generation. The system pairs AMD Ryzen 9000 CPUs with NVIDIA or AMD GPUs and can also handle simulations and ...
A Reddit comparison uses VRAM per dollar and rated bandwidth to navigate GPUs discussed in LocalLLaMA communities. Prices were gathered with ChatGPT, new or second-hand, with acknowledged inaccuracies. A rough method, but useful for anyone evaluating...
A developer releases Jenny, an open source Electron app for running LLMs locally with tool calling, rollback and an IDE. No network calls beyond the local runtime; safeguards on destructive commands and file edits. Windows first, with reports of soli...
A guide shows how to connect llama.cpp, the MCP protocol and FreeCAD to generate solids with a local model. The workflow uses Qwen3.8-27B quantized Q4_K_M and an mmproj-F16 vision projector: the model can call tools, read screenshots and verify geome...
A custom llama.cpp branch brings expert expansion to MoE models and tests it on Apple Metal. The author says it beats their DS4 version, but cross-platform validation is missing. The case highlights how local inference for sparse models still depends...
A local comparison with Q8_K_XL quantization on two 128GB StrixHalo nodes shows that DeepSeek-V4-Flash-Vision generates tokens more slowly than Qwen3.8-Flash-Next, yet completes tasks in about half the time. The author observes fewer hallucinations a...
The Qwen3.8-Flash-Next-oQ4e-mtp variant shows local inference speeds on Apple Silicon: 45 tokens per second on M4 Max and 25 on M2 Ultra, according to llm-bench.io. The source also notes similar speed to Qwen3.8 27B on Apple Silicon. Useful for self-...
A technical post shows how Qwen3.8 27b in Q4_K_XL quantization runs on an RTX 3090 with 24 GB of VRAM and 100k token context. The author argues that small self-hosted LLMs capable of 80-90% of repetitive work erode cloud vendor revenue more than fron...
