Qwen3.8 Flash-Next
PulseAugur coverage of Qwen3.8 Flash-Next — every cluster mentioning Qwen3.8 Flash-Next across labs, papers, and developer communities, ranked by signal.
- developed by Qwen3.8-Flash 95%
- instance of Qwen4 90%
- developed by Qwen4 90%
- developed Qwen3.8-Flash 90%
- used by Darwin-180B-RSI 90%
- used by Qwen Sparse Attention 90%
- used by N-gram Embedding 90%
- developed Qwen4 90%
- developed by Darwin-180B-RSI 90%
- developed Qwen Sparse Attention 90%
- competes with GLM-5.3-Flash 70%
- affiliated with Qwen4 70%
- 2026-09-16 product_launch GSQ-RCO released new GGUF quantizations for the Qwen3.8-Flash-Next model, reducing file size and improving performance. source
- 2026-08-27 product_launch Alibaba's Qwen team released the experimental open-weight model Qwen3.8-Flash-Next, previewing the Qwen4 architecture with NVIDIA's day-zero fine-tuning support. source
- 2026-08-26 product_launch Alibaba Group released Qwen3.8-Flash-Next, a new mixture-of-experts model. source
- 2026-08-26 product_launch Alibaba released and open-sourced the Qwen3.8-Flash-Next model, a multimodal MoE model previewing the Qwen4 architecture. source
- 2026-08-26 product_launch Alibaba released and open-sourced the Qwen3.8-Flash-Next model, featuring a new architecture and improved cost efficiency. source
- 2026-08-26 product_launch Alibaba's Qwen team released Qwen3.8-Flash-Next, an open-weight multimodal MoE model previewing the Qwen4 architecture. source
- 2026-08-26 product_launch Alibaba released Qwen3.8-Flash-Next, a new multimodal model with extensive context window support and day-zero GPU compatibility. source
- 2026-08-26 product_launch Qwen announced the release of Qwen3.8-Flash-Next, a new multimodal open-weight model. source
- 2026-08-25 product_launch The Qwen3.8-Flash-Next language model is scheduled for release. source
- 2026-08-24 product_launch Alibaba's Qwen team released Qwen3.8-Flash-Next, a multimodal MoE model previewing the Qwen4 architecture. source
- 2026-08-24 product_launch Alibaba launched Qwen3.8-Flash-Next, a multimodal MoE model previewing the Qwen4 architecture. source
10 day(s) with sentiment data
Qwen4 to integrate Per-Layer Embedding and Sparse Attention for improved efficiency
The experimental Qwen4-Exp model previews novel architectures like Per-Layer Embedding (PLE) and Qwen Sparse Attention (QSA). These are designed to increase model capacity without a proportional increase in compute costs, suggesting that the upcoming Qwen4 series will likely feature these optimizations for enhanced efficiency and performance.
Qwen3.8-Flash-Next's multimodal MoE architecture will spur new multimodal applications
The release of Qwen3.8-Flash-Next as a multimodal MoE model with a 1M context window, incorporating innovations like GDN+QSA and N-gram embeddings, suggests a push towards more capable and efficient multimodal AI. This combination of features is likely to enable developers to build novel applications that can process and generate richer, more complex multimodal content.
Qwen3.8-Flash-Next demonstrates significant CPU performance via quantization and offloading
The Qwen3.8-Flash-Next model, despite its large size, can run on a CPU at a reasonable speed (8.34 tokens/sec) using 4-bit quantization and llama.cpp. This highlights a trend towards making large models accessible on less powerful hardware, especially when the embedding table is offloaded to the GPU, indicating a viable path for on-device or lower-resource AI applications.
-
Strata enables 125B LLM on gaming PCs via low-bit quantization
Strata is a new open-source project that enables running large language models, specifically a 125-billion-parameter Qwen3.8-Flash-Next model, on consumer-grade gaming PCs with as little as 12GB of VRAM. It achieves thi…
-
180B AI model runs on gaming laptop via MoE and SSD streaming
VIDRAFT has released POCKET-Darwin-180B, a 180-billion-parameter AI model designed for deployment on consumer hardware, including gaming laptops. This model utilizes a sparse Mixture-of-Experts architecture, SSD streami…
-
Local Qwen3.8-Flash-Next challenges Claude Opus 5.5 in coding task
A user compared the performance of Qwen3.8-Flash-Next running locally on their Strix Halo laptop against Anthropic's Claude Opus 5.5 for a complex coding task. While Claude Opus 5.5 was significantly faster, completing …
-
Local AI models Qwen3.8 and DeepSeek-V4.1 power agentic coding tasks
Two separate Mastodon posts highlight the use of local AI models for coding tasks. One user details agentic coding on a Strix Halo laptop using Qwen3.8-Flash-Next, comparing its performance to Opus 5.5. Another post, wr…
-
Open 180B Model Darwin-180B-RSI Leads 10 Hugging Face Benchmarks
An open-weight model named Darwin-180B-RSI has achieved the top position on 10 official Hugging Face leaderboards, surpassing all other participating organizations. This model, built by VIDRAFT on Alibaba's Qwen3.8-Flas…
-
Bidraft leads Hugging Face benchmarks with 10 first-place finishes
Bidraft has achieved first place in 10 categories on the Hugging Face official leaderboard, more than any other organization. The company's models also excelled in new 'practical' benchmarks, IFStruct and ExtractBench, …
-
Huihui-Qwen3.8-Flash-NEXT model released on Arint.info
The Qwen3.8-Flash-NEXT model, also referred to as Huihui-Qwen3.8-Flash-NEXT, has been released and is available on Arint.info. This model is associated with Strata and is being shared via Mastodon. Hugging Face is also …
-
Darwin-180B-RSI leads 9 Hugging Face benchmarks with recursive self-improvement
VIDRAFT's Darwin-180B-RSI model family has achieved the top position in 9 official Hugging Face benchmarks, surpassing all other participating organizations. This open-source model utilizes a recursive self-improvement …
-
Qwen3.8-Flash-Next model exhibits GPU offloading behavior
The Qwen3.8-Flash-Next model, when run on four GPUs with automatic device mapping, leaves the first GPU unused and offloads 22 GB of data. This behavior suggests potential inefficiencies or specific configurations in ho…
-
Qwen model hallucinates Alibaba Cloud signed URL, raising data exfiltration fears
A user reported that the Qwen3.8-Flash-Next language model hallucinated a signed URL pointing to Alibaba Cloud storage. This occurred while the model was performing product research on Amazon, and the generated URL incl…
-
User plans dual R9700 GPU setup for local AI model training
A user is planning to upgrade their local AI setup by adding a second R9700 GPU to their existing build. The goal is to improve performance and enable the running of larger models like Qwen3.8-Flash-Next at higher quant…
-
Intel Hybrid CPU users can triple LLM decode speed with Strata calibration
A Reddit user on the r/LocalLLaMA subreddit shared a "PSA" recommending that users with Intel hybrid CPUs run Strata's calibration tool. This calibration reportedly nearly tripled their local LLM decode speed, improving…
-
64GB RAM limits LLM users to choosing between models or other workloads
A user on r/LocalLLaMA shared their experience with running large language models (LLMs) on a system with 64GB of RAM, highlighting the challenges of memory limitations. While they found that the Strata framework signif…
-
Qwen3.8 Flash Next model optimized for local inference on diverse hardware
The Qwen3.8 Flash Next model is being optimized for local inference across various hardware configurations. One user achieved 400 tokens/sec on an RTX 6000 with a fork of NInfer, while another demonstrated running the m…
-
Strata project enables large local LLM execution on consumer GPUs
The Strata project enables running large language models like Qwen3.8-Flash-Next locally on consumer hardware, including GPUs with 12GB VRAM. It achieves this by distributing the model's workload across the GPU, CPU, RA…
-
133 GB MoE model runs on 8 GB GPU via NVMe streaming
A technical blog post details a method for running a large 133 GB Mixture-of-Experts (MoE) model, Qwen3.8 Flash-Next, on a consumer-grade 8 GB GPU. The technique involves streaming model experts from NVMe storage and ut…
-
VIDRAFT releases 180B LLM runnable on consumer laptops
VIDRAFT has released POCKET-Darwin-180B, a 4-bit quantized version of their 180-billion-parameter Darwin-180B-RSI model. This version is compatible with llama.cpp and can run on consumer hardware, including laptops with…
-
Qwen3.8-Flash-Next achieves 200 tok/s decode on local hardware
A user on Reddit reported impressive performance metrics for the Qwen3.8-Flash-Next model running on a power-limited setup. Utilizing Strata software on a 5090 GPU with 96GB of DDR5 RAM, the system achieved decode speed…
-
User optimizes Qwen3.8 Flash-Next on Huawei Ascend hardware
A user has successfully optimized the Qwen3.8 Flash-Next model for inference on a custom hardware setup featuring two Huawei Ascend 310P3 cards, each with 48 GB of memory. This configuration, initially struggling with c…
-
Qwen3.8-Flash-Next performance questioned on R9700 system
A user on Reddit's r/LocalLLaMA subreddit is seeking feedback on the performance of the Qwen3.8-Flash-Next model running on their R9700 system. They are experiencing 863 tokens/s during prefill and 35 tokens/s during de…