NVIDIA GeForce RTX 5070 Ti

Headlines · 2

1. 1d
   Developer Reports 110K Qwen3.8-27B Context on RTX 5070 Ti

   Community developer u/jayecin shared a llama.cpp configuration that sets a 110,000-token context for a custom-weight Qwen3.8-27B GGUF model on an RTX 5070 Ti. The developer reports generation at about 55 tokens per second, with roughly 1.5GB of VRAM to spare. The setup uses the Ryzen 9800X3D integrated GPU for display output, q8_0 and q4_0 KV caches, and flash attention. The report includes configuration and runtime logs, but the performance figures have not been independently validated.

   r/LocalLLaMA · 🔥 2
2. 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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