mw00 / project-maya

Project Maya - run GLM-5.3-Flash (321B MoE) and DeepSeek-V4.1-Flash-1017 (748B MoE) privately on your own GPU(s), from one card to 16, NVIDIA or AMD, Linux or Windows. Engine, server, dashboard, OpenAI- and Anthropic-compatible API, quant tools.

VecSzn / PolyStrata

Run large AI models on your own PC: Qwen3.8-Flash-Next on Strata's engine, and GLM 5.3 Flash, DeepSeek V4 Flash, LFM2.5, Qwen3.6, Ornith 1.5, Gemma 4, Tiel Coder and MiniCPM-V on a second engine written for PolyStrata. One-click install for Windows and Linux, OpenAI/Anthropic API on localhost.

Indras-Mirror / Xolotl

Xolotl (formerly Strata-DSV4.1): DeepSeek-V4.1-Flash, all 552B, on one gaming PC (RTX 4090 + 90 GB RAM) at ~27 tok/s, plus GLM-5.3-Flash, Qwen3.8-Flash-Next and Qwen3.8-27B. A heavily extended fork of Strata: MoE experts tiered across VRAM, pinned RAM and NVMe; OpenAI + Anthropic API.

CSUNX233 / qwen3.8-27b-ninfer-5070tilaptop-kvmem-local-agent

RTX 5070 Ti Laptop 12GB 跑 Qwen3.8-27B：NInfer + KVMem、256K 逻辑上下文、RAM KV、MTP、快速缓存续接与 dsh 本地 Agent / 搜索 / 浏览器。含源码、Windows 部署及实测。

IIIIIllllIIIIIlllll / Qwenox

An inference framework customized for AI MAX 395, compatible with both Linux and Windows, and supports deploying only qwen3.8-flash-next. This project is for reference and learning purposes only. 为AI MAX 395定制的推理框架，Linux和Windows通用，只能部署qwen3.8-flash-next。本项目仅供参考学习使用。

shawnshekari / strixite

LLM inference engine written from scratch in HIP for AMD Strix Halo (gfx1151) and Qwen3.8-Flash-Next - ~50 tokens/s out to 492k context

Tylogi / TyloQuant

Get more intelligence from every bit. Better quantization formats and smarter calibration let larger, stronger models run smoothly on the hardware you already own.

1115714829 / llama.cpp-v100

llama.cpp optimized for NVIDIA Tesla V100 (SM70): Qwen3.8-Flash-Next with MTP (prefill 3402 tok/s at 200K context on 6x V100-16GB; also runs on 2x V100-16GB) and Qwen3.8-27B with DFlash2 (decode up to 220 tok/s); 2-6 GPUs, 262K-512K context, multimodal, concurrent serving, built-in web UI.

hardenedlinux / SonicBoom

A compiler-first PyTorch alternative. MLIR-based Boom IR, Guile scripting, CUDA and CPU. On consumer GPUs it already beats llama.cpp’s LLM decode with the same kernels; the compiler makes the difference. GPLv3.

JakeATX / llamAmpere

llama.cpp fork for significantly improved performance on Ampere (especially RTX 3090 / 3090 Ti): example: 95+ tok/s over a 100K-token generation at temperature 1 for qwen3.8, supporting full 262K ctx. Supports low memory (12gb) configs for 200k+ ctx

sauravsingla / MemVanta

Low-memory C++20 LLM inference runtime for quantized GGUF models on CPU — mmap-backed weights, paged KV cache, Q4/Q8 kernels, and reproducible llama.cpp benchmarks.

feder-cr / jev

jevos: an open-source alternative to Jev and Microsoft-Decision-1. Yes/no decisions, choices and scores with probabilities, on your laptop's CPU.

omgitsbase / llmash

Ollama-compatible LLM server on a tuned llama.cpp fork. Same commands, API and model store as Ollama, up to 4x Ollama's decode speed and 2.8x vLLM's in our benchmarks, from custom CUDA kernels, MTP speculation and its own 3-bit builds. Uncensors models in one command. Windows and Linux, NVIDIA.