Release21d ago
llama.cpp v0.6.0 ships GLM-5.3-Flash (320B) and Qwen4Exp MTP speculative decoding, a new llama_batch_ext API, and up to ~3x faster few-row mat-mul on Apple GPUs
ggml-org released llama.cpp v0.6.0 on Oct 5 (v0.5.0 was Sep 23). It brings GLM-5.3-Flash (GLM5-Next, a 320B text+vision hybrid MoE) into a stable tag, adds MTP speculative decoding for Qwen4Exp (~1.5x decode on DGX Spark), introduces the llama_batch_ext / llama_process() API for mixed token and embedding batches, adds few-row MMA mat-mul kernels on Metal (up to ~3x for speculative and batched decoding), and bumps ggml to v0.26.0. Session file formats are bumped too.
Tooling24d ago
llama.cpp ships Decision Models: /v1/systemone scores options in one forward pass (Julia-1, Laya, Kev-4B, lev, OpenJev)
On 2026-10-02 ggml-org announced llama.cpp server support for Decision Models via /v1/systemone (PR #29818, merged 2026-10-02T09:56Z). Send a state plus typed questions; get option probabilities in one forward pass. Day-0 models: Julia-1, Laya, Kev-4B, lev, OpenJev (vision). Cloudflare Clef is planned next.
Benchmark37d ago
RTX 3090 local coding-agent bake-off: Qwen3.8-27B Q4 + llama.cpp ~59 tok/s with reliable tools
@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.
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