Custom RDNA4 Kernels Boost Qwen3.8 Performance Up To 30x

By PulseAugur Editorial · [1 sources] · 2026-08-31 01:13

A developer has created a custom set of kernels, named R9V, designed to optimize performance for RDNA4 graphics cards, specifically targeting AMD's R9700s. When applied to the vLLM-Radiance inference engine, these kernels significantly boost the speed of models like Qwen3.8-Flash-Next, achieving up to a 30x increase in performance for certain tasks. The project also includes work on a separate inference engine for dense models and offers packages for Qwen3.8-Flash-Next and Muse Glimmer 30B, with performance comparisons against other backends like llama.cpp. AI

IMPACT Optimizations for RDNA4 hardware could enable wider adoption of local LLMs on AMD GPUs.

RANK_REASON Developer-created optimization kernels for specific hardware. [lever_c_demoted from research: ic=1 ai=0.7]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Custom RDNA4 Kernels Boost Qwen3.8 Performance Up To 30x

How we ranked this

Signal score: 2 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; Developer-created optimization kernels for specific hardware. [lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: infra, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

r/LocalLLaMA TIER_1 English(EN) · /u/Public_Umpire_1099 · 2026-08-31 01:13

R9V: A designer set of kernels I've been working on for R9700s/RDNA4. Qwen3.8-Flash-Next Unsloth IQ4_XS (w/ TP on 2 R9700s, MTP, SSD n-gram, 128k ctx, vision): TG256 of *78 tok/s* (~3x increase), PP8192 of *1510 tok/s* (~30x increase).

<div class="md"><h1>TL;DR: Ninfer/DS4 but for RDNA4</h1> <p>Highly custom kernels built for RDNA4, applied to vLLM-Radiance to greatly improve Qwen3.8 Flash Next speeds. This is mostly for dual R9700s with preferably 48GB of RAM or higher, but feel free to tinker. …

How we ranked this

COVERAGE [1]

R9V: A designer set of kernels I've been working on for R9700s/RDNA4. Qwen3.8-Flash-Next Unsloth IQ4_XS (w/ TP on 2 R9700s, MTP, SSD n-gram, 128k ctx, vision): TG256 of *78 tok/s* (~3x increase), PP8192 of *1510 tok/s* (~30x increase).

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R9V: A designer set of kernels I've been working on for R9700s/RDNA4. Qwen3.8-Flash-Next Unsloth IQ4_XS (w/ TP on 2 R9700s, MTP, SSD n-gram, 128k ctx, vision): TG256 of 78 tok/s (~3x increase), PP8192 of 1510 tok/s (~30x increase).