Nvidia GeForce RTX 5090

Headlines · 4

1. 3d
   Nvidia GeForce RTX 5080 Prices Reach $2,000 Amid Scarce Stock

   Tom’s Hardware reports that the Nvidia GeForce RTX 5080’s lowest cited in-stock price at a reputable online seller reached $1,999 through Best Buy, while other listings reached $2,299.99 and $2,983.23. Availability was limited, with most online stock offered by third-party sellers and local pickup at Best Buy restricted to one or two units. The article also links AI-driven prioritization of high-margin data-center products to reduced attention and memory allocation for consumer GPUs.

   Tom’s Hardware · 🔥 0
2. 4d
   llama.cpp PR fuses MoE matrix multiplications and GLU in MMQ

   A llama.cpp pull request proposes fusing MoE gate/up matrix multiplications and GLU in the CUDA MMQ path. The PR reports prompt-processing gains of 5.7%–17.2% across DGX Spark and RTX 5090 systems using several Qwen MoE model and quantization configurations. The supplied report does not establish that the change has been merged or released, and the benchmark figures have not been independently verified.

   r/LocalLLaMA · 🔥 0
3. Oct 21
   Developer runs DeepSeek V4.1 Flash IQ1_S on RTX 5090 at 53.6 tok/s

   A developer posting on r/LocalLLaMA reports running DeepSeek V4.1 Flash 1017 at Unsloth's UD-IQ1_S quantization in Basalt, an inference engine they are developing, on an RTX 5090 plus an RTX 5060 Ti 16 GB and 128 GB of DDR5. With all 107 GiB of experts held in RAM and the 5090 used as an expert cache, the setup reached 53.6 tok/s decode and about 610 tok/s prefill, versus roughly 20 tok/s for a llama.cpp PR #30817 baseline on the same hardware and quantization. Letting the 5060 Ti compute some experts raised decode to 63.5 tok/s, but the author reports no perplexity or KL-divergence quality measurements yet, only greedy-match agreement with llama.cpp on test prompts.

   r/LocalLLaMA · 🔥 0
4. Oct 19
   Qwen3.8-27B and Qwen3.8-Flash-Next local inference benchmark roundup

   An r/LocalLLaMA user, FeydRowan, compiled single-request decode speeds for Qwen3.8-27B and the 125B MoE Qwen3.8-Flash-Next on consumer hardware, marking rows as “verified” only when backed by a LocalMaxxing run or a paired run on the same machine. The author's own capy-flash rows were measured on 2× RTX 5070 Ti with 96 GB DDR5, while most other figures are as reported by each project; the post also compares tensor parallelism versus layer splitting, expert offload, quant formats and drafters, and calculates tokens per second per €1,000 using EU VAT-inclusive prices. It puts 2× R9700 on top for Flash-Next cost-efficiency and says 96 GB of DDR5 is about 40% of the bill for such builds, but the performance and price data are largely user-compiled and self-reported.

   r/LocalLLaMA · 🔥 0

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