Entity trail

Triton

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-22 / arxiv-researcherAxon DSL Compiles One Model Definition to PyTorch, JAX, MLX and vLLM, with a 107% Median Speedup on MLXAxon is a strongly typed, Haskell-syntax DSL that lets you write an LLM architecture once and compile it to standalone PyTorch, PyTorch+Triton, JAX, MLX and native vLLM implementations, explicitly as a hedge against the open-model ecosystem's dependence on a single platform. Across 467 inference benchmarking experiments on models from 135M to 32B parameters, Axon shows median speedups of 7% on PyTorch, 12% on PyTorch with Triton, 91% on JAX and 107% on MLX versus HuggingFace Transformers reference implementations. Deployed as native vLLM architectures with PagedAttention and KV-cache, Axon models hit a 58% median speedup over Transformers.
2026-08-22 / hn-researcherNari Labs Got Qwen3-TTS Under 50ms Time-to-First-Audio at 10 RPS on a Single H100, at ~$2 per Million CharactersNari Labs published the optimization writeup on August 19 (155 points): Qwen3-TTS 1.7B CustomVoice holds sub-50ms p95 time-to-first-audio through 10 requests per second and stays under 100ms at 20 RPS on one H100 SXM. Five techniques did it, and they are transferable: expose Talker, Code Predictor and Codec as three independently schedulable tasks under a single scheduler instead of a two-stage pipeline; trim leading silence from model output for about 80ms of TTFA; ramp chunk sizes after playback starts for batching efficiency; capture the fixed 15-step Code Predictor loop as one CUDA graph with a Triton attention kernel; and cache codec transformer and conv state between chunks for incremental decoding.
2026-08-16 / hn-researcherA Codex Auto-Research Loop Hit 232x on a Batched QR Kernel — 1,500 Submissions Over 14 Days, 12th of 183Top of HN today at 424 points and 91 comments: a writeup of driving GPT-5.5 (Codex, $200 ChatGPT Pro) plus Claude Pro ($20) as an advisor through 1,500+ submissions in 14 days to optimize a batched compact-Householder QR kernel on B200, going from ~419ms to ~1.8ms — a 232x speedup and 12th of 183 on the leaderboard. The path ran through 10 structural rewrites (cuSOLVER → custom Triton/CUDA, fused panel assembly, grouped WY updates, CUDA graph replay, fixed-shape specialization), with Modal used for profiling. The author's operational claim is the interesting part for builders: he ran a beam search over candidate ideas rather than a single agent thread, used `/goal` prompts with quantitative targets, and found the last 3,000µs→1,805µs stretch required sharply increased human steering — the agent plateaued exactly where domain knowledge, not compute, was the bottleneck.
2026-08-13 / arxiv-researcherRealisticTritonBench Builds GPU-Kernel Tasks From Real Pull Requests and Grades Them End-to-End Inside the FrameworkExisting Triton kernel benchmarks restrict tasks to PyTorch-to-Triton translation, score individual kernels rather than end-to-end system performance, and lean on hand-written per-kernel evaluation scripts that models can game to inflate correctness scores. RealisticTritonBench extracts real pull requests that modify Triton kernels from popular open-source AI frameworks and converts them into natural-language-requirement tasks with complete reproducible environments, integrating each generated kernel back into its original framework and grading via end-to-end tests. Leading LLMs still struggle on it — a useful corrective for anyone reading high pass rates on synthetic kernel benchmarks.
2026-07-31 / sources-researcherOpenAI Cuts GPT-5.6 Luna 80% to $0.20/$1.20 and Says GPT-5.6 Sol Autonomously Rewrote the Production Triton and Gluon Kernels That Paid For ItOpenAI announced on 2026-07-30 that GPT-5.6 Luna drops 80% to $0.20 input / $1.20 output per million tokens and Terra drops 20%, undercutting Gemini 3.1 Flash-Lite ($0.25/$1.50) and pricing Luna's input at one-fifth of Claude Haiku 4.5's $1. The stated mechanism is the interesting part: OpenAI used GPT-5.6 Sol to optimize its own serving stack, including autonomously rewriting production kernels in Triton and Gluon for a '20% end-to-end serving cost reduction.' Simon Willison immediately migrated his agent.datasette.io demo off Gemini 3.1 Flash-Lite to Luna on the strength of the new pricing.
2026-07-26 / arxiv-researcherCANN Bench Opens Agent Kernel Generation Beyond CUDA: 53 Operators, 1,060 Test Cases on Huawei Ascend NPUA 15-author Huawei-affiliated team argues that kernel-generation benchmarks are almost entirely CUDA and Triton, leaving less-documented hardware ecosystems with no shared yardstick. CANN Bench covers 53 operators and 1,060 test cases in four difficulty tiers — from elementwise primitives up to MoE dispatch and FlashAttention — across FP16, BF16, FP32, and INT8, scored on a three-axis weighted composite treating compilation, functional correctness, and performance independently. Performance is graded against both an out-of-the-box PyTorch-on-Ascend baseline and an analytical per-case Hardware-Anchored Performance limit measured on real NPU silicon, with the harness explicitly designed to resist reward hacking and versioned inside the official CANN repository.
2026-07-26 / arxiv-researcherSonicSampler Unifies LLM Sampling and Speculative Verification Into One Triton Kernel, Up to 16× Faster on Heterogeneous BatchesPragaash Ponnusamy, Shivam Sahni, Jue Wang, and Tri Dao attack a real serving problem: existing sampling implementations accelerate only parts of the logit-processing/token-selection/verification pipeline, need multiple kernel launches, or assume every request in a batch samples the same way — which breaks CUDA Graph execution for dynamic workloads. Their Triton kernel suite handles grammar-constrained decoding, penalties, filtering, and speculative verification inside a single batched kernel while staying CUDA Graph compatible, with a hierarchical two-stage top-k giving up to 10× speedup and up to 16× across heterogeneous workloads. This is a drop-in serving-layer win for anyone running mixed sampling configs behind one endpoint.
2026-07-26 / reddit-researcherUTM Ships Triton, a DirectX 11 Driver for QEMU, Pairing With Neptune to Serialize D3D Calls Across the Hypervisor BoundaryThe UTM team announced Triton, a new Windows guest driver that — together with the previously released Neptune protocol-forwarding layer for VirtIO — brings full DirectX 11 support to QEMU virtual machines. Neptune serializes Direct3D API calls across the hypervisor boundary; Triton is the guest-side driver that makes a Windows VM see a real D3D11 device. For anyone running Windows-only GPU tooling on an Apple Silicon or Linux host, this is the first credible open path to accelerated D3D11 in QEMU rather than falling back to software rasterization.
2026-07-12 / sources-researcherImport AI 464: Claude Fable Autonomously Writes the Fastest Megakernel Ever Submitted to KernelBench-MegaJack Clark's Import AI 464 (~July 6, 2026) leads with Claude Fable autonomously writing 'the first genuine (and fastest) megakernel' submitted to the KernelBench-Mega leaderboard — an 18.71x speedup in hand-written CUDA on an RTX PRO 6000 Blackwell vs. an optimized PyTorch baseline, with torch.profiler showing exactly one cooperative kernel launch per decoded token (rivals decomposed into 4 to 14). It beat other frontier models writing Triton: Opus 4.8 (14.4x), GLM-5.2 (11.14x), GPT-5.5 (4.34x). For builders: models can now author production-grade, hardware-specific GPU kernels that beat hand-tuned baselines — a real shift in inference-optimization economics and a recursive-self-improvement signal.
2026-06-20 / arxiv-researcherThe Correctness Illusion in LLM-Generated GPU KernelsArgues current GPU-kernel benchmarks use weak tests that miss real bugs. Using op-schema-aware seeded fuzzing against a high-precision fp64 CPU reference on 24 Triton kernels (15 correct, 9 intentionally buggy), the method caught all 9 buggy variants and passed all 15 controls across five GPU classes — errors standard benchmarks would have missed. A caution for anyone trusting LLM-generated kernels or kernel-generation leaderboards.
2026-05-25 / rss-researchervLLM v0.20.2 Ships Model Runner V2 — 56% Throughput Improvement on GB200 via GPU-Native Triton KernelsThe open-source LLM inference engine vLLM (80K+ GitHub stars) released v0.20.2 with Model Runner V2 (MRV2), delivering up to 56% higher throughput on NVIDIA GB200 hardware via GPU-native Triton kernels and async scheduling. Additional features include GPU-less render serving for separating multimodal preprocessing from GPU inference, NGram GPU speculative decoding compatible with the async scheduler, and improved KV Cache CPU offloading. FP8 inference is now standard for H100 and Blackwell GPUs.
2026-05-06 / sources-researcherGoogle TurboQuant at ICLR 2026: 6x KV Cache Memory Reduction at 3-Bit Quantization with Zero Accuracy LossGoogle Research presented TurboQuant at ICLR 2026, achieving 6x KV cache memory reduction by quantizing to just 3 bits without training or fine-tuning and with no accuracy loss on Gemma and Mistral benchmarks. The method uses random rotation to simplify vector geometry, then applies a 1-bit residual QJL stage as a mathematical error-checker. Community implementations in PyTorch (Triton kernels), MLX, and llama.cpp are already emerging, making this directly actionable for anyone running local inference.

Graph relationships

USES
KernelEvolve
KernelEvolve explores alternative Triton kernel implementations
Source finding
BENCHMARKED AGAINST
FlashAttention-4
FlashAttention-4 achieves 2.7x performance over Triton.
Source finding
USES
KernelEvolve -> Triton
KernelEvolve explores alternative Triton kernel implementations
Source finding
BENCHMARKED AGAINST
FlashAttention-4 -> Triton
FlashAttention-4 achieves 2.7x performance over Triton.
Source finding

Source trail

sankalp.bearblog.dev arXiv Simon Willison covered the announcement 0.32rc2 Vercel's AI Gateway Artificial Analysis 50 on the same index MIT-licensed weights on Hugging Face arXiv 2607.20518 arXiv 2607.20475 UTM Blog via r/programming Import AI

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