Niko1221 / Strata
Qwen3.8-Flash-Next on any consumer hardware: one-click install for Windows / Linux. Strata inference engine, OpenAI/Anthropic API on localhost, optional image input.
Niko1221 / Strata
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.
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.
robertkarl / UnbiasedStrata
Strata minus the bullshit. clone, rsync to prod, compile and go
jesdga95 / basalt
Local inference engine for Qwen3.8-Flash-Next on NVIDIA Blackwell
yxhpy / qwen3.8-flash-next-local-deploy
Qwen3.8-Flash-Next (125B MoE) 单卡 16G 本地部署实测：Strata + GSQ-RCO IQ3_XXS，Ubuntu 24.04
surya-koritala / condor
Qwen3.8-Flash-Next (180B MoE) on one 12-16 GB NVIDIA GPU, Linux or Windows. Own engine, own compression.
peonist-ai / halogen-flash-server
The fastest way to run Qwen3.8-Flash-Next on Strix Halo (gfx1151)
hongfaqiu / strata-manager
Strata Manager makes Niko1221/Strata, the local LLM inference engine, easier to set up and use. Manage local models and services, monitor hardware and API usage, chat and tune inference. Open-source desktop app for Windows / Linux x64.
MiaAI-Lab / Qwen3.8-Flash-Next-Single-DGX-Spark-TensorFold
Qwen3.8 Flash Next on one DGX Spark (TensorFold)
sergqwer / strata-nvfp4
Strata fork: Qwen3.8-Flash-Next 125B MoE in NVFP4 on one RTX 20-50 card (12 GB+) and 64 GB of RAM or more. Setup installs our GPTQ quants (huihui abliterated, OrcaRouter) from Hugging Face. CPU experts on AVX-512 or AVX2 (+AVX-VNNI), all experts in RAM from 92 GB, a low-RAM mode below, vision, W4A8 prompts. Windows / CUDA 13.
youssofal / MTPLX
The fastest way to run Qwen 3.8 Flash Next, Qwen 3.8 27B and Ternary Bonsai 2 27B on a Mac: 125 tok/s in OpenCode on an M5 Max, and a 27B model on 16 GB Macs. Native MTP speculative decoding on Apple Silicon, exact at any temperature. OpenAI and Anthropic compatible local server.
jpezzulli / sglang-rtxpro6000
Optimized SGLang runtime for Qwen3.8-27B FP8 with DFlash2 and Qwen3.8 Flash-Next NVFP4 with FR-Spec on one NVIDIA RTX PRO 6000 Blackwell 96 GB GPU (SM120): 524K context, HiCache and NIXL.
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
MiaAI-Lab / Qwen3.8-Flash-Next-Single-DGX-Spark
Qwen3.8-Flash-Next on ONE DGX Spark (TP=1)
blazux / qwen3.8-Flash-DGX
recipe for running Qwen3.8-Flash-Next on a single DGX Spark
tonyd2wild / Qwen3.8-Flash-Next-NVFP4-DGX-Spark
Qwen3.8-Flash-Next (NVFP4) on DGX Spark in vLLM: one Spark 43.9 tok/s with our disk-backed n-gram table patch, staged gather and reduced-vocab MTP draft; TP2 SPEED 53.7; TP4 CONTEXT 9M KV pool. Launchers, patch, 40-prompt harness, KV ledger, credits. SGLang TP2 day-0 lane kept.
yibie / strata-mlx
Qwen3.8-Flash-Next on Apple Silicon: an MLX engine that reads Strata's GGUF files and speaks its line protocol (unofficial)
sjespersen / homelab-agent
Started as a €70 used mini PC running an always-on AI agent (Hermes, n8n, Pi-hole, monitoring, Tailscale, encrypted backups). Now with a second stack next to it: a homelabbed GPU server for local AI (Qwen 27B and Flash-Next, image generation, Ollama, one API). All as code.
carloslfu / slotstream
Run a 105 GB AI model on a Mac that can't hold it. Slotstream streams Qwen3.8-Flash-Next (125B mixture of experts) from your SSD and caches the busiest experts in memory, so it runs on Macs with 16 to 64 GB. One native Swift binary on MLX and Metal, no Python, offline. Works with Claude Code, Codex and Ollama or OpenAI clients.
QwenLM / Qwen3.8-Flash-Next
Qwen3.8-Flash-Next is the foundation model developed by Qwen Team, Alibaba Group.
MiaAI-Lab / Qwen3.8-Flash-Next-Dual-DGX-Sparks
Qwen3.8-Flash-Next-NVFP4-vLLM-Dual-DGX-Spark
myllmbox / qwen38-flash-next-recipe
qwen38-flash-next recipe for solo GB10
Wallawalla47 / Infernix
C++/CUDA single-GPU inference engine for Qwen3.8 (incl. Qwen3.8-Flash-Next with offloaded experts) on the RTX 5090; grew from NInfer
myllmbox / qwen38-flash-next-cluster-recipe
qwen38-flash-next recipe for tp=2 GB10
0xBakeer / qwen38-flash-next-spark
Run Qwen3.8-Flash-Next (180B) on a single DGX Spark by keeping its 51B n-gram embedding table on NVMe
DominikBucko / qwen38-flash-next-2x3090
Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).
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。
architectds / collabosm
A local app that rents a GPU in your own Colab account and serves Qwen3.8 on it: 27B on an A100-40G, or Flash-Next (125B-A6B MoE) on an A100-80G. The cost is shown before it starts; chat with images; Codex or any OpenAI client uses a fixed local /v1. Auto-stop, CU ledger, live prefill/decode. Windows, macOS, Linux.
azampatti / Qwen3.8-Flash-Next-Int4-FAST
Recipe for the fastest 4bit qwen3.8FlashNext
vcruz305 / Qwen3.8-Flash-Next-EXL3-DGX-Spark-recipe
Serve Qwen3.8-Flash-Next (turboderp ExLlamaV3 pack) on one NVIDIA DGX Spark with exllamav3
sxuff / qwen38-flash-next-dgx-spark
Verified Qwen3.8 Flash-Next GGUF recipe for one NVIDIA DGX Spark or ASUS Ascent GX10
fulvian / engraft-ngram
ENGRAFT: write facts into an LLM's n-gram (Engram) memory table as a removable overlay: 100 facts in a 9 MB file, no weights touched, verified on llama.cpp GGUF (Qwen3.8-Flash-Next).
Apolog1ze-Dev / QwFNfer
A custom inference engine tuned for Qwen4 architecture. Currently runs Qwen3.8-Flash-Next at 15+ tok/s on 16GB of VRAM and 30 GB of RAM
1115714829 / llama.cpp-v100
llama.cpp optimized for NVIDIA Tesla V100 (SM70): large MoE Qwen3.8-Flash-Next with MTP (prefill 3464 tok/s at 200K context, 6x V100-16GB) and Qwen3.8-27B with DFlash2 (decode up to 225 tok/s); 2-6 GPUs, 262K-512K context, multimodal, concurrent serving.
azampatti / GB10-3.8-Flash-Next
A script to launch Qwen3.8-Flash-Next in a single DGX-Spark (or its variants) at 40+ tok/s inference speed.
MiaAI-Lab / Qwen3.8-Flash-Dual-DGX-Sparks-TensorFold
Qwen3.8-Flash-Next on two DGX Sparks with TensorFold
dg1kjd / sglang-sxm2
Agentic-coding oriented inference engine for N x V100-SXM2, based on SGLang: GLM-5.3-Flash, Qwen3.8-Flash-Next, DeepSeek-V4.1-Flash, MiniMax-H3.
gabrielolympie / sglang-flashnext-sm120
Qwen3.8-Flash-Next 180B NVFP4 @ 231 tok/s on one RTX PRO 6000 Blackwell (sm120) - sglang patches, launchers, full optimization analysis
gpillon / ignis
OpenAI-compatible inference server for Qwen3.8 on one RTX 5090 / RTX PRO 6000: the 27B at ~170 tok/s (peaks ~370), Flash-Next (125B MoE) at ~100 tok/s, built for local coding agents. Rust + CUDA, Windows and Linux.
nguyenthimy2022kg-alt / Qwen-Flash-SM80-170HX
双 SM80 / CMP 170HX 的 Qwen3.8-Flash-Next 社区 vLLM：NVIDIA NVFP4、GDS、TEP2、MTP6，支持 1～10 并发，词表与 PCIe 通信优化。中英文部署与可构建源码。
dolf3131 / dgx-spark-llm-serving
Serving large LLMs on a single DGX Spark (121 GiB): Qwen3.8-Flash-Next on TensorFold/SparkFold EXL3 and vLLM NVFP4, Nemotron 3.5 Super VL on vLLM; what ran, what it measured, what did not work
julianmb / haloq38flash
qwen3.8-flash-next on strix halo — 91g quant, 56 tok/s, 262k context, receipts included