Qwen 3.8 27B

Headlines · 3

1. 2d
   RTX Spark review: beats M5 Max in local AI, lags in gaming

   Tom's Hardware tested Nvidia's RTX Spark platform in a top-trim Microsoft Surface Laptop Ultra carrying the full RTX Spark Superchip with 20 CPU cores, 6,144 CUDA cores and 128GB of LPDDR5X-9400 memory. Running Artificial Analysis's new end-to-end agentic benchmark AgentPerf-local with Qwen 3.8 27B and Qwen 3.6 35B A3B, the RTX Spark system finished about 25% faster than a 14-inch MacBook Pro with Apple's M5 Max, while AMD's Ryzen AI Max+ 395 (Strix Halo) needed roughly twice as long, about 40 minutes, on the heavier test. Gaming is a weaker point: roughly 300 GB/s of shared memory bandwidth, Arm CPU cores and the overhead of the Prism x86 emulator leave frame rates and input latency behind dedicated RTX 50-series gaming laptops, and the reviewer leans on DLSS Frame Generation to compensate. The outlet notes that launch embargoes limit this first round to platform-wide results, with battery life, build and display details to follow.

   Tom’s Hardware · 🔥 3
2. 3d
   Databricks cuts Qwen 3.8 27B fine-tuning time 6x on AI Runtime

   In a blog post, Databricks describes how systematic performance work on its AI Runtime cut fine-tuning time for Qwen 3.8 27B on a fixed dataset from 7 hours 44 minutes to 1 hour 17 minutes — about 6x — from a baseline throughput of 9,396 tok/s. The team notes that the initial run already showed 98% average GPU utilization, but that metric only measures whether the GPU is doing anything; the real bottleneck was hundreds of thousands of small ops that the CPU could not enqueue fast enough. Installing fused kernels such as flash-linear-attention and causal-conv1d brought training down to 2 hours 5 minutes (3.7x), and switching to 1D packed tensors with correct position_ids to cut padding waste delivered the final 6x gain, with example code published in an AI Runtime cookbook.

   Databricks Blog · 🔥 0
3. 3d
   Jailbroken PS5 runs 27B local AI models at 20 tokens/sec

   GitHub developer cobanov previously ported llama.cpp to the PS5 as the community project PS5LM, but it ran as a CPU payload limited to roughly 6.25 GiB of RAM and mainly handled small 0.8B and 2B models. Reddit user u/AdIllustrious7246 then forked it into a native app that reaches the console's GPU through PS5_Vulkan, a port of the RADV graphics driver, raising usable memory to about 11.4 GiB and running Qwen 3.8 27B with low-bit quantization (such as IQ2_XXS) at roughly 20 tokens per second. The developer also reported about 61 tokens/sec for Gemma 4 E4B and 124 tokens/sec for Llama 3.2 3B, and added a dashboard showing live generation speed, GPU/CPU usage, power draw and temperatures, plus an OpenAI API-compatible interface for LAN access. All figures are the developer's own publicly posted results, require a jailbroken console and specific firmware (docs list 7.00 to 13.60), and are not an official Sony feature.

   cnBeta · 🔥 6

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