llama.cpp Qwen3Next Graph Optimization Merged, LocalLLaMA Reports Faster Inference

What the community post tracked

The r/LocalLLaMA post 1r4hx24 centered on llama.cpp PR #19375, titled models : optimizing qwen3next graph. At collection time, the post had 173 upvotes and 54 comments. The pull request was created on 2026-02-05T20:57:37Z and merged on 2026-02-14T10:57:36Z by maintainer ggerganov.

Technical scope of PR #19375

The PR description states its goal clearly: rework the ggml compute graph to avoid unnecessary copies. According to GitHub metadata, it includes 19 commits, 4 changed files, +262 additions and -299 deletions. Most changes are in src/models/qwen3next.cpp, with related updates in CUDA and Metal paths. This indicates a structural inference-path optimization rather than a narrow benchmark tweak.

Measured performance impact

The PR body publishes benchmark tables for M2 Ultra and DGX Spark. Reported speedups vary by test type and quantization level, generally falling in the 1.09x to 1.38x range. In several tg32 and pp tests, gains are around the mid-20% to high-30% band. Community commenters also reported real-world improvements, including around 17% TPS gains in mixed CPU/GPU setups and larger jumps in selected configurations.

Why this matters for local LLM workflows

Higher token throughput on unchanged hardware budgets

Lower latency for interactive coding and agent loops

Better viability of large Qwen3Next checkpoints on prosumer rigs

A stronger baseline for upcoming follow-up optimizations

The broader takeaway is that local LLM competitiveness depends heavily on inference-engine engineering, not only on model weights. For teams running self-hosted stacks, engine updates like this can materially shift cost-performance tradeoffs without requiring new GPUs.

Sources: llama.cpp PR #19375, Reddit discussion