🚀 RELEASES & LAUNCHES
NVIDIA quietly releases Nemotron Labs Dory 1.0 4B, a looped latent-recurrent LLM
NVIDIA posted Nemotron Labs Dory 1.0 4B, a looped latent-recurrent 4B model, on Hugging Face in four quantizations with no announcement.- Aimed at local/general-purpose reasoning users wanting a small-footprint model, released October 16, 2026 under the permissive OpenMDW 1.1 license for commercial and non-commercial use.
- Dory 1.0 4B is a 4B dense model with a normalized-GPT architecture: 8-layer input block, 20-layer shared recurrent block, and 8-layer output block, with interleaved sliding-window and full attention layers.
- It supports up to 256K-token context (262,144 positions validated in vLLM) and ships in BF16, MXFP8, NVFP4, and Q5_K_M GGUF formats.
- The looped design lets a single checkpoint trade inference compute for accuracy by varying loop count, with no retraining or model swap, and improves token efficiency.
- The release appeared with no NVIDIA blog post or hosted demo, so third-party verification of NVIDIA's self-reported benchmarks is still pending.
Adaptive KV streaming lets Qwen3.8-27B run full 262k context on 16GB GPUs
A community llama.cpp fork streams the KV cache from system RAM, letting Qwen3.8-27B run its full 262k-token context on 16GB consumer GPUs.- Aimed at local-LLM users on 16GB consumer GPUs (AMD RX 9070 XT, NVIDIA RTX 5070 Ti / 4070 Ti Super) running Qwen3.8-27B.
- Adaptive KV Cache Streaming dynamically streams each full-attention layer's KV cache into VRAM on demand via a ring buffer, instead of keeping it all resident.
- Results: Qwen 27B IQ4_XS with Q8/Q4 KV at full 262,144-token context on a 16GB RX 9070 XT at 80+ t/s decode; on a 4070 Ti Super, 33.68 t/s at 200K context vs 8.04 t/s for stock llama.cpp.
- Original CUDA implementation by RaymondHuang210129 was ported to AMD ROCm/HIP; ASCII-condensed IQ4_XS weights (~13.6GB) leave ~3.4GiB VRAM headroom vs ~7.4GiB KV needed at 262k.
- Upstreaming is being discussed in llama.cpp discussion #28216; the approach trades host RAM and PCIe bandwidth for usable context and is still experimental.
Sources 📎 Coverage: github.com · 👁 via r/LocalLLaMA
⚠️ INCIDENTS & SAFETY
- OpenCode RCE flaw (GHSA-632h-h47v-g4x4) let malicious websites run commands; fixed in 1.18.22
Datadog researchers disclosed a remote code execution vulnerability in OpenCode's /global/upgrade endpoint, fixed in version 1.18.22.- Affects users of OpenCode, the open-source AI coding agent with 16 million monthly users and 200,000+ GitHub stars, who installed it via npm, pnpm, or Bun.
- Datadog's Christophe Tafani-Dereeper found that a content-type confusion in the /global/upgrade API let a malicious webpage trigger installation of an attacker-controlled npm tarball whose preinstall script executes arbitrary code on the victim's machine.
- The exploit works cross-origin via a top-level form POST to http://127.0.0.1:4096/global/upgrade; the web server runs unauthenticated by default unless OPENCODE_SERVER_PASSWORD is set.
- 82 vulnerable versions received more than 647,000 npm downloads from September 17–23, 2026; OpenCode 1.18.22 fixes the flaw, and Anomaly declined to request a CVE.
- Users should upgrade to 1.18.22+ and rotate any credentials exposed in OpenCode sessions, as the Reddit thread advises.