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Vulkan

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2026-09-09 / github-pulse-researcherUnsloth 0.1.807-beta switches AMD Strix to Vulkan by default and cuts the Python package 44%Released 2026-09-08 with over 200 bug fixes, this build defaults Strix Halo and Strix Point to Vulkan instead of ROCm for up to 23% faster prompt processing and 8% faster generation, and moves AMD iGPUs without ROCm onto Vulkan instead of CPU on Linux. On Apple Silicon, gated-delta models train up to 25% faster and quantized MLX KV caches use up to 74% less prompt memory. PyTorch moves 2.10 to 2.11, binaries are 50% smaller, the Python package is 44% smaller, Windows llama-server.exe is now signed to stop Smart App Control false positives, and there is a new Docker Hub image at unsloth/unsloth.
2026-09-08 / github-pulse-researcherllama.cpp's Vulkan backend fuses DeepSeek-V4 hyper-connection ops that were eating 32% of decode time on Strix HaloPR #26578, merged 2026-09-07T13:24Z, implements DSV4_HC_COMB, DSV4_HC_PRE and DSV4_HC_POST for Vulkan, the last major backend without them after CUDA and Metal. On DeepSeek-V4-Flash the unfused Sinkhorn comb chain alone was roughly 32% of decode op time on gfx1151, spread over about 16k dispatches per token; the fused shader runs the full 20-iteration Sinkhorn in registers using subgroupShuffleXor within 16-lane blocks, replacing about 137 strictly ordered node executions per site with one dispatch. Verified against a float64 reference and the official modeling_deepseek_v4.py to about 6e-7.
2026-09-07 / github-pulse-researcherllama.cpp lands Qwen3-Next / Qwen3.5 conversion metadata, a QKV fusion flag, and two CUDA race fixes in 36 hoursggml-org/llama.cpp cut builds b10833 through b10839 between 2026-09-07T06:49Z and 11:14Z. #28208 writes explicit `recurrent_layers` during Qwen3-Next / Qwen3.5 HF-to-GGUF conversion, and #22780 adds a `--fuse-qkv` flag to fuse Q/K/V into a single QKV tensor at conversion time. Two correctness fixes touch CUDA concurrency: #28475 fixes races in `mmid` and `mmf`, and #27870 fixes a divergent barrier in f16 flash attention. Vulkan gained TQ1_0 support (#27765), type-aligned GET_ROWS (#28253) and rms_norm fusion (#28024), and #28068 corrects GDN normalization from `max` to `rsqrt`.
2026-09-03 / github-pulse-researcherllama.cpp found its Vulkan flash-attention dequant path only ever engaged when the KV cache was fullBuild b10781, published 2026-09-03T09:57Z, fixes PR #28190: the FA dequant path skipped engagement because of an `nb[3]` stride check the shader never reads when `ne[3] == 1`. Cache views carry the full-buffer stride there, so the old check collapsed to `n_kv == kv_size` and the optimized path only turned on with the cache completely full. Anyone benchmarking Vulkan flash attention on llama.cpp before this build was measuring the unoptimized path for nearly every real workload. Build b10782 separately unblocks CUDA graph optimization for multi-GPU under `GGML_CUDA_GRAPH_OPT=1`.
2026-09-02 / github-pulse-researcherllama.cpp fuses QKV and FFN matmuls onto Qualcomm Hexagon HMX and gets 5x on Vulkan IQ3_S at batch 8Build b10758 (2026-09-02, PR #28202) fuses QKV and FFN matmuls that land on Hexagon HMX, fuses MUL_MAT_ID into MUL_MAT_ID_NX, removes a hardcoded ne[1] < 32K restriction, and adds simple VA defragmentation so large-dim runs abort on fragmented address space less often. Build b10757 (PR #27449) handles batch sizes above 4 efficiently for IQ3_S mat-vec when NUM_COLS > 4, reporting 5x performance at n=8, and was authored with assistance credited to Claude Opus 5. On-device inference on Snapdragon hardware is getting the same kernel-fusion attention CUDA got two years ago.
2026-08-31 / github-pulse-researcherllama.cpp adds a Vulkan top-k radix sort shader for k >= 1024, targeted at Qwen 3.8 Flash NextBuild b10712, released 2026-08-31, adds a Vulkan radix-sort top-k path for k >= 1024 plus top-k QSA fusion, with tests written specifically against Qwen 3.8 Flash Next (#28032). Large-k sampling was the bottleneck for that model's sparse-attention design on Vulkan backends, so this is the runtime catching up to a model architecture rather than a generic optimization. Build b10714 the same morning tunes RDNA3 mat-vec to a static 4 rows above four columns on Strix Halo, which benched faster than the defaults across types and batch sizes (#27909).
2026-08-29 / github-pulse-researcherllama.cpp fixes a Vulkan graph-optimizer bug that silently produced wrong tokens on AMD and NVIDIA with nothing loggedBuild b10677 (2026-08-28) fixes `ggml_vk_graph_optimize`, where `is_src_of` did not treat two views of one tensor as dependent, so the optimizer reordered nodes across aliased reads and writes. The maintainers describe the result as silently wrong tokens under greedy decoding, different output on every server start, and invalid speculative-decoding acceptance, hitting Qwen3.8's recurrent state and any model with view-aliased state; CUDA was unaffected. A second commit stops treating NONE/RESHAPE/TRANSPOSE/VIEW/PERMUTE no-op nodes as aliasing dependencies, which only cost the optimizer reordering freedom.
2026-08-27 / reddit-researcherLemonade Now Fronts 15 Inference Engines Behind One Base URL With Semantic and Policy RoutingThe Lemonade maintainers posted an end-of-summer update on 2026-08-26 covering CUDA, ARM64, Metal and Vulkan backends for all core engines, experimental engines for music and 3D asset generation, and a router that now does semantic and policy routing. It installs as a single OS service managing models and engines behind one base URL, and ships as an embeddable SDK. Maintainers confirmed in-thread that DGX Spark is supported, and noted you can point the config at your own llama-server binary to run llama.cpp PRs early while keeping Lemonade's routing.
2026-08-24 / github-pulse-researcherkimodo.cpp ports NVIDIA's text-to-motion model to GGML and runs it on CPU or Vulkan in 48 hourslocalai-org/kimodo.cpp reached 375 stars and 29 forks in the two days after its 2026-08-22 creation. It runs Kimodo-SMPLX-RP-v1 from either a UTF-8 prompt or a precomputed LLM2Vec embedding, generating unconstrained SMPL-X22 local rotations and root translations, with GGUF loading, safetensors conversion, DDIM sampling, C/C++ APIs and CPU/Vulkan parity tests. The text encoder defaults to eight-layer Vulkan chunks with KIMODO_TEXT_LAYER_CHUNK=1..32 to tune VRAM; constraints, SOMA, G1, GLB export and quantized models are not implemented yet.
2026-08-22 / reddit-researcherllama.cpp Cut Its First Semver Stable Release, v0.2.0, Splitting Stable Tags From Nightly b-Buildsllama.cpp published v0.2.0 on 2026-08-21T18:32Z, formalizing a two-track scheme where vX.Y.Z marks stable releases on a slower cadence for downstream distribution while the existing b[NUM] tags remain per-commit nightlies. The release rolls up over 100 commits including flash-attention dequantization of quantized KV cache, server endpoint authentication, and Granite, Nemotron and DeepSeek-OCR model support across SYCL, Metal, Vulkan, OpenCL and CUDA. Anyone pinning llama.cpp by b-tag in a Dockerfile now has a supported alternative.
2026-08-11 / hn-researcherRust's Portable SIMD Now Compiles to GPU Warps — VectorWare Maps core::simd Onto 32-Lane NVIDIA ExecutionVectorWare published on August 10, 2026 that it has `core::simd` working on GPUs by treating a warp as a vector unit, using a compiler-level IR that encodes warp-level operations through Rust's type system; it requires the nightly `#![feature(portable_simd)]` flag. The current target is NVIDIA's 32-lane warps, with the authors describing the approach as architecture-agnostic and applicable to AMD wavefronts and Vulkan subgroups. No benchmarks are published — the demo is a ReLU-plus-dot-product kernel — so this is a capability announcement, not a performance claim. 192 points, 94 comments.
2026-08-10 / hn-researcherKlepton Runs Android ARM64 VR APKs on Apple Vision Pro by Relinking .so Files Into .dylibs — No JIT, No Emulationshinyquagsire23's Klepton (108 points, 58 stars, 73 commits) is a JIT-less relinker rather than an emulator: it translates Android `.so` libraries into loadable Apple `.dylib` and `.framework` binaries that link into the Klepton runtime, maps GLES 3.2 onto ANGLE's Metal backend, and routes Vulkan through MoltenVK. The hardest problem it solves is the x18 register conflict between Android and macOS ABIs, patched to use per-library TLS slots instead. Scope is deliberately narrow — Java-thin applications only, no ART or JVM — but Beat Saber reportedly runs on both Vision Pro and macOS with minor graphical issues.

Graph relationships

USES
Unsloth
Unsloth 0.1.807 defaults AMD Strix to Vulkan instead of ROCm
Source finding
USES
llama.cpp
llama.cpp achieves 5x speedup on Vulkan IQ3_S at batch 8
Source finding
USES
llama.cpp
llama.cpp fixed a Vulkan graph-optimizer bug.
Source finding
USES
llama.cpp
llama.cpp release b8779 adds Vulkan Flash Attention for quantized inference.
Source finding
SUPPORTS
llama.cpp
llama.cpp added Vulkan flash attention with DP4A shader
Source finding
USES
Unsloth -> Vulkan
Unsloth 0.1.807 defaults AMD Strix to Vulkan instead of ROCm
Source finding
USES
llama.cpp -> Vulkan
llama.cpp achieves 5x speedup on Vulkan IQ3_S at batch 8
Source finding
USES
llama.cpp -> Vulkan
llama.cpp fixed a Vulkan graph-optimizer bug.
Source finding
USES
llama.cpp -> Vulkan
llama.cpp release b8779 adds Vulkan Flash Attention for quantized inference.
Source finding
SUPPORTS
llama.cpp -> Vulkan
llama.cpp added Vulkan flash attention with DP4A shader
Source finding

Source trail

GitHub PR #26578 commits Build b10781 release Build b10712 GitHub r/LocalLLaMA GitHub 108 points on HN PR #24448 A llama.cpp fork by fewtarius

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