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Infra2026-08-28 · source-backed
Up from $4,299.99 in June against a $1,999 launch MSRP, with Korean listings at $5,112 (r/LocalLLaMA). Memory is now over 80% of a GPU's bill of materials, with 16GB of GDDR7 climbing from about $65-80 per card in mid-2025 to over $200 by year end. Local inference economics changed underneath everyone this year, and it wasn't the models.
Each link below shares sources, entities, or timing with this story.
Three signals hit in the same week. That's not coincidence, that's a threshold. First: Qwen 3.6-35B-A3B is running at 44 tokens per second on a single 16GB GPU at Q4 quantization with 100K context. It's a 35B-parameter MoE model with only 3B active parameters per token. A 293-...
PR #19378 landed in llama.cpp this week, and I think most people are underselling what it means. Backend-agnostic tensor parallelism via --split-mode tensor makes multi-GPU inference work across AMD, Intel, and Apple Silicon. Not just CUDA. Everything. For context: llama.cpp h...
DeepSeek-V4-Flash-0731 landed July 31 under MIT with a DSpark speculative-decoding module attached. Terminal Bench 2.1: 82.7. Toolathlon-Verified: 70.3. DSBench-FullStack: 68.7. DeepSWE: 54.4. NL2Repo: 54.2. The model card claims it beats DeepSeek-V4-Pro (Preview) "despite its...
Alibaba released Qwen3.6-27B on April 22. Dense architecture. Open weights. 77.2% on SWE-bench Verified, within 3.7 points of Claude Opus 4.6. On SkillsBench, it scores 48.2% versus its own 397B MoE predecessor's 30.0%. That's a 77% improvement with 14.8x fewer parameters. Let...
An r/LocalLLaMA post reports a working deployment on 80x RTX 5090 connected over 25 gigabit Ethernet rather than NVLink or InfiniBand — roughly 2.5TB aggregate VRAM against a ~594GB MXFP4 weight file, surplus absorbed by activation and KV overhead. Expert-parallel MoE over com...
For about a year, "run your agent locally" meant accepting a model that couldn't reliably call a tool twice in a row. That excuse is gone. Meta Superintelligence Labs published Muse Glimmer today: a 29.6B dense causal transformer, 52 layers, 6,656 hidden dim, with a ~1.8B ViT-...
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