Releases·Tool Release
AgentionAI releases Qwen3.8-Flash-Next AP GGUF quants benchmarked against other high-quality quants
Agention AI released high-precision AP quantized GGUFs of Qwen3.8 Flash on Hugging Face, reporting benchmark results beating other high-quality quants. The team developed a modified KLD measurement with a new dataset because the model's n-gram memorization of Wikipedia skewed standard evaluation. The quants balance precision with prefill performance.
2 engagement1 sourceWed, Sep 2, 2026, 10:10 PM
AI Overview
- AgentionAI released Qwen3.8-Flash-Next-AP GGUF quants it says beat other high-quality quants on its benchmarks, with prefill performance kept in mind
- The team used a modified KLD measurement with a new dataset because NGRAM quants memorized nearly all of Wikipedia and skewed results
- Model card published on Hugging Face at agentionai/Qwen3.8-Flash-Next-AP-GGUF
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Related
Social·Community
Qwen 3.8 flash for a single spark
A community member released a low-bit quantization of the Qwen 3.8 Flash base model on Hugging Face. The quant reportedly retains 95% of bfloat16 accuracy without degradation at long contexts and reaches roughly 40 tokens/s, leaving headroom for 256k context and RoPE. Inference improvements are still in progress.
A community member released a low-bit quantization of the Qwen 3.8 Flash base model on Hugging Face. The quant reportedly retains 95% of bfloat16 accuracy without degradation at long contexts and reaches roughly 40 tokens/s, leaving headroom for 256k context and RoPE. Inference improvements are still in progress.
3 engagement1 sourceredditFri, Oct 2, 2026, 11:27 AM
Social·Community
Tsinghua SIGS lab releases VeriLoop E2, a Qwen3.8-27B Apache-2.0 post-train, with no official GGUF yet
A Reddit post highlights VeriLoop E2, a newly released Apache-2.0 27B model post-trained from Qwen3.8-27B for code, math, scientific reasoning, and agentic workloads. The release ships full safetensors weights on Hugging Face but no official GGUF/Ollama build, prompting discussion of which quantization and deployment targets the local-inference community should prioritize.
A Reddit post highlights VeriLoop E2, a newly released Apache-2.0 27B model post-trained from Qwen3.8-27B for code, math, scientific reasoning, and agentic workloads. The release ships full safetensors weights on Hugging Face but no official GGUF/Ollama build, prompting discussion of which quantization and deployment targets the local-inference community should prioritize.
6 engagement1 sourceredditWed, Sep 23, 2026, 12:43 PM
Developments·Benchmark
Community benchmark tests Qwen3.8-Flash-Next NVFP4 on local agentic coding
A community benchmark run compares the NVFP4-quantized Qwen3.8-Flash-Next against 27B-class and other local models on agentic coding tasks. Results are published on a public local-LLM benchmark site, with a companion post on r/BlackwellPerformance covering single-RTX Pro 6000 performance. It matters to practitioners choosing quantized models for local coding agents.
A community benchmark run compares the NVFP4-quantized Qwen3.8-Flash-Next against 27B-class and other local models on agentic coding tasks. Results are published on a public local-LLM benchmark site, with a companion post on r/BlackwellPerformance covering single-RTX Pro 6000 performance. It matters to practitioners choosing quantized models for local coding agents.
156 engagement2 sourcesredditFri, Aug 28, 2026, 05:30 PM
Social·Community
Qwen3.8-Flash-Next breaks 94% on cupel benchmark in early local tests
A developer reports early local testing of Qwen3.8-Flash-Next on an M4 Max 128GB Studio, achieving over 94% on the cupel benchmark—the first model this year to do so. The model uses the qwen4_exp architecture, which is not yet fully supported by oMLX, requiring workarounds.
A developer reports early local testing of Qwen3.8-Flash-Next on an M4 Max 128GB Studio, achieving over 94% on the cupel benchmark—the first model this year to do so. The model uses the qwen4_exp architecture, which is not yet fully supported by oMLX, requiring workarounds.
195 engagement1 sourceredditThu, Aug 27, 2026, 12:34 PM
Releases·Model Release
ISTA-DASLab releases GSQ-RCO GGUFs for Qwen3.8-Flash-Next plus 50% expert-pruned Coder build at ~1.89 bpw
ISTA Deep Algorithms and Systems Lab released GGUF quantizations of the 176.9B-parameter sparse MoE model Qwen3.8-Flash-Next using two new methods, GSQ and RCO. Four quantized builds run from 2.40 to 3.50 bpw, and a Coder build removes half the routed experts, yielding an effective ~1.89 bits per original parameter and a 29.6 GB resident footprint that fits a single 32 GB accelerator. Benchmarks show the 3.50 bpw build matching BF16 on every evaluated benchmark.
ISTA Deep Algorithms and Systems Lab released GGUF quantizations of the 176.9B-parameter sparse MoE model Qwen3.8-Flash-Next using two new methods, GSQ and RCO. Four quantized builds run from 2.40 to 3.50 bpw, and a Coder build removes half the routed experts, yielding an effective ~1.89 bits per original parameter and a 29.6 GB resident footprint that fits a single 32 GB accelerator. Benchmarks show the 3.50 bpw build matching BF16 on every evaluated benchmark.
164 engagement1 sourceredditTue, Sep 29, 2026, 08:40 AM