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I turned an asymetric pair of Tesla V100s PCIe both (16 GB + 32 GB) into a surprisingly capable local LLM lab — 1.38k prompt tok/s, 40 decode tok/s with qwen3.8 27B Q6 and Q8...

TL;DR: I run a mismatched Tesla V100-PCIE pair—one 16 GB card and one 32 GB card, 48 GB total—in a Proxmox/LXC-based local-inference lab. The practical winner so far is a recent CUDA build of llama.cpp with tensor split, Flash Attention, --numa distribute , and large batches. On Qwen3.8 27B Q6_K_M…

Read the full story at r/LocalLLaMA ↗

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  1. 2026-09-19 20:16 · r/LocalLLaMA
    I turned an asymetric pair of Tesla V100s PCIe both (16 GB + 32 GB) into a surprisingly capable local LLM lab — 1.38k prompt tok/s, 40 decode tok/s with qwen3.8 27B Q6 and Q8...

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  1. M2 Mac ultra128gb Qwen flash next — r/LocalLLM
  2. Qwen3.8-Flash-Next-Heretic2-IQ4XS on Halogen Flash Server vs llama-server on Strix Halo: 2.3-7.7x prefill speedup with half the VRAM (+ vision works on BYO GGUF) — r/LocalLLM
  3. Multi-hour llama.cpp optimization experiments on Qwen MoE models, patches, benchmarks, and reproduction guides — r/LocalLLM
  4. The bear can dance: Qwen 3.8 27B on one 3090 for 3 weeks — r/LocalLLaMA
  5. CUDA: enable sparse fa for qwen4 by am17an · Pull Request #28770 · ggml-org/llama.cpp — r/LocalLLaMA
  6. focus-llama: a llama.cpp fork implementing Declarative Attention (arXiv:2609.02737) — r/LocalLLaMA
  7. I ran Opencode and PI against the same local model on 3 identical projects, same prompts, same hardware... — r/LocalLLM
  8. I benchmarked 13 model/quant configs on a GPU with no tensor cores (Vega iGPU + Vulkan) and wrote it up as a measurement study — the quant encoding suffix matters more than you'd think — r/LocalLLM

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