Open LLMs with massive parameter counts and long contexts are increasingly run on consumer and edge hardware thanks to aggressive quantization, pruning, and dedicated runtimes. The ecosystem around llama.cpp and specialized local stacks is quickly integrating new models, MTP support, and decision modules.
A user assembled a fully local inference setup with GLM 5.3 Flash on two DGX Sparks, running vLLM with two-way tensor parallelism. Reported figures: prefill at about 1,500 tokens/s and decode at about 40 tokens/s with a 100k-token context. Claude Cod...
A GitHub project runs an INT4 Qwen3.5-9B LLM on ex-mining SQRL FPGAs. Two FK33 boards at 75 MHz deliver 3.2 tok/s generation at short context and 2.4 tok/s between 2k and 3k tokens. Estimates for Qwen3.8-27B reach 25 tok/s with four XCVU35P dies at 2...
Aleph Alpha publishes the Kolibri-1 card: 78 billion total parameters, 3.46 billion active, and a 1 million token context under Apache 2.0. AI-RADAR analysis shows the constraint shifting from compute to memory and KV cache, with direct implications ...
Ultra-specialized runtimes like Strata and ninfer give up the generality of llama.cpp and vLLM to optimize a few models and one hardware family. The bet: maximum output from fixed local configurations, even at the cost of disposable code. For self-ho...
The Kyojin project runs two roughly 300-billion-parameter MoE models on a single 128 GB mini PC with AMD Strix Halo. Measured on a Ryzen AI Max+ 395, GLM-5.3-Flash and MiMo-V2.6-Flash reach up to 650 tokens/s prefill and 26 to 44 tokens/s decode. EXL...
Aleph Alpha releases Kolibri-1, an LLM with 78 billion parameters, 3.46 billion active per token, and up to 1 million tokens of context under Apache 2.0. The combination touches three constraints of self-hosted deployments: memory, compute, and licen...
A pull request in the llama.cpp repository introduces an optimization that halves the indexer score memory for Qwen Flash Next. The change, reported on Reddit, results in lower VRAM usage during inference. For on-premise and self-hosted deployments, ...
The llama.cpp project has announced a new feature called Decision Models, signaling an expansion beyond pure text generation. For those running Large Language Models locally, the move suggests a maturation toward more actionable outputs, with implica...
Pull request #29818 by ngxson introduces /v1/systemone in the llama.cpp server and references five GGUF models: Laya, Julia-1, Lev, OpenJev, and Kev-4B. No technical details are provided, but for self-hosted deployments the expanding API surface rais...
PR #29761 from am17an brings MTP support to Qwen Flash Next in llama.cpp, with GGUF quantizations already available on Hugging Face. A signal for local-stack teams: self-hosted ecosystems are shortening the path from model release to practical adopti...
A pull request reopens MTP integration for Qwen Flash Next in llama.cpp, with GGUF quantizations available on Hugging Face. The work is still WIP; for local LLM operators it becomes a test bed for reducing latency without hardware changes, but with a...
As reported, Ling-3.1-flash combines 560 billion total parameters, 25 billion active per token, and a context window up to one million tokens, with a two-week free trial and then an open-source release. The profile points to a sparse architecture: lo...
Pull request #27773 by timkhronos adds GLM-5.3-Flash (GLM5-Next) support to llama.cpp. The model can now run on home computers without cloud services. A signal for on-premise deployments: open source runtimes are consolidating as a distribution chann...
AMD introduces PerfOpt in Linux 7.4 to improve AI/LLM performance on integrated Radeon graphics. Benchmarks show 18-23% gains on Ryzen hardware, especially entry-level. A signal for local inference: iGPUs become a more credible option for lightweight...
AMD's new 256-core EPYC delivers memory bandwidth at 91% of an RTX 5090 through a 16-channel DDR5-12800 controller. The LocalLLaMA community responds with sarcasm and realism about the cost of high-density RDIMMs and the question of how many tokens p...
ISTA DASLab released quantized GGUFs for Qwen3.8-Flash-Next and a Coder variant with half the experts removed. The GSQ and RCO combination brings the resident working set to 29.6 GB, within a 32 GB accelerator, with benchmarks near or above BF16. A s...
