AI Coding Live News

@Brjen’s team spent a week comparing local coding-agent stacks on one RTX 3090 (24GB). Winner: Qwen3.8-27B Unsloth UD-Q4_K_XL with llama.cpp (65k context, q8 KV, native MTP depth 2, fully on GPU). Real multi-step coding runs hit ~59 tok/s (~46 tok/s at 45k context) and passed nested tool JSON, fixing a failing test, and a clean unified diff. They rejected Q3 for bad tools, heavier quants for context/MTP tradeoffs, and Flash-Next setups that needed CPU offload.

Unsloth says optimized local GGUF decoding makes GLM-5.3-Flash up to ~3.3× faster (typically ~1.6–3.4×), with multi-token prediction (MTP) and faster long-context decoding. 3-bit quants still target ~128GB setups via Unsloth Desktop or llama.cpp; docs and Hugging Face GGUF weights are updated.

Alibaba Qwen released open-weight Qwen3.8-Flash, a multimodal Mixture-of-Experts model billed as an early preview of the Qwen4 architecture. The production-scale checkpoint is 125B parameters plus 51B N-gram embeddings, with only 6B activated per token. The new stack combines Gated DeltaNet plus Qwen Sparse Attention, Gated Residual branches, N-gram embeddings that can live in host memory, and a refined Muon optimizer. Qwen said it trained at about one-ninth the cost of Qwen3.7-Plus while beating it across the board, especially on coding and office work: 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision with CI. Native context is 262K tokens, extendable to 1M with YaRN; at 1M context the QSA kernel is up to 7.6x faster in prefill and 4.9x faster in decode. QwenCloud lists roughly $0.15 input / $0.47 output per million tokens, and day-0 runtimes arrived from NVIDIA NeMo, SGLang, Unsloth, OpenRouter, and OpenCode Go.

Unsloth released 3-bit GGUF quantization for Z.ai's GLM-5.3-Flash (Ox Alpha), enabling local on-device inference for the 320B-A18B multimodal MoE on 128GB RAM workstations without datacenter GPUs.

Alibaba Qwen team open-sources Qwen3.8-Flash-Next weights (125B total / 6B active MoE). Partnering with Unsloth, optimized GGUF quantizations run full 262K context on single 24GB GPUs at 85 tokens/sec.

Alibaba Qwen confirmed that the 125B multimodal MoE Qwen3.8-Flash-Next can now run locally on about 75GB of RAM via Unsloth GGUFs, with N-gram embeddings offloaded to CPU or unified memory for near-VRAM speed. It is the day-0 local path for the Qwen4 architecture preview weights.