KTransformers: Running DeepSeek-R1's Full 671B Model on a Single 24GB GPU

China has killed the GPU mafia 🤯 They open-sourced a framework that runs DeepSeek-R1 (671B params) on a single 24GB graphics card. → 3-28x faster inference → Fine-tune DeepSeek-V3 on 4x 4090s (~80GB) → 6-12x faster than ZeRO-Offload → Runs Kimi-K2, GLM, Qwen3-Next out of the box 17.3k stars. 100% Open Source. https://lnkd.in/dD5DwJ_s

This is a real, well-documented open-source project (KTransformers), and the post's technical claims are accurate — though the framing is dramatic.

Confirmed accurate:

• The framework is real: KTransformers (kvcache-ai/ktransformers), developed by Tsinghua University's MADSys Lab (KVCache.AI) in partnership with APPROACHING.AI and 9#AISoft — a genuinely Chinese-origin, Apache-2.0 licensed project, with work published at SOSP 2025 (a top systems conference).
• "3–28x faster inference" on a single 24GB GPU — confirmed verbatim: the project's own changelog states "Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup."
• DeepSeek-R1 (671B) on a single 24GB card — confirmed; the mechanism is a CPU-GPU hybrid approach where attention/KV cache stay on GPU while MoE experts are offloaded to system RAM, using AMX-accelerated kernels.
• Fine-tuning DeepSeek-V3 on 4x RTX 4090s (~80GB total) — confirmed directly from the project's own README: DeepSeek-V3 fine-tuning uses "~80GB total" GPU memory across "4x RTX 4090."
• "6-12x faster than ZeRO-Offload" — confirmed verbatim from the project's own Chinese README: "相对 ZeRO-Offload 加速: 在基准 MoE SFT 任务中训练速度提升 6-12 倍" ("Speedup relative to ZeRO-Offload: 6–12x faster training speed on benchmark MoE SFT tasks").
• Runs Kimi-K2, GLM, Qwen3-Next out of the box — confirmed; the project's release notes explicitly list day-0/native support for Kimi-K2, GLM4-MoE, and Qwen3-Next, among others (also Qwen3-MoE, MiniMax, DeepSeek-V3.2/V4).
• 17.3k stars — closely matches an independent source citing "17,179 Stars" for the project.
• 100% open source — confirmed, Apache-2.0 license.

Worth noting as framing, not fact:

• "China has killed the GPU mafia" is hyperbolic editorializing, not a factual claim — the actual achievement is a clever CPU-GPU hybrid inference/fine-tuning technique, not the elimination of any "mafia." It's an efficiency breakthrough for a specific use case (running huge MoE models on modest hardware), not a wholesale disruption of GPU-based computing generally.
• The generation/decode speed in practice is more modest than "3-28x" might suggest in isolation — independent reporting notes decode speeds around 14 tokens/sec on a single 24GB card (with prefill hitting up to 286 tokens/sec), which is usable but far from datacenter-class throughput. The multiplier is real relative to comparison baselines (like llama.cpp), not an absolute speed guarantee.

Verdict: Every specific technical claim and number in this post is directly verifiable against KTransformers' own documentation and independent reporting. The only "fluff" is the attention-grabbing "GPU mafia" framing layered on top of solid technical facts.