{"slug": "ktransformers-flexible-llm-inference-framework", "title": "KTransformers – Flexible LLM Inference Framework", "summary": "KTransformers, a flexible LLM inference framework, enables deployment of 100B+ parameter models locally on a single RTX 5090 (32GB VRAM) using CPU/GPU heterogeneous computing without quantization. The framework achieves 2,540 tokens/s prefill speed and 27.6 tokens/s decode speed on a MiniMax-M2.1 FP8 model with a single RTX 5090 and dual AMD EPYC 9355 CPUs, delivering a 4.5x prefill speedup over llama.cpp Q8_0 quantization.", "body_md": "# Low-VRAM, Full-Precision Inference\n\nKTransformers uses CPU/GPU heterogeneous computing, leveraging CPU memory and compute\n\nto deploy top-tier 100B+ parameter models locally with just a single RTX 5090 (32GB VRAM).\n\n# Low-VRAM Full-Parameter Fine-Tuning\n\nFine-tune 100B+ parameter models with full parameters on consumer GPUs — no expensive multi-GPU clusters needed.\n\n## Why KTransformers?\n\nBuilt for developers who want to run large models on accessible hardware without sacrificing performance.\n\nHeterogeneous Computing\n\nOptimize inference using CPU, GPU, and other accelerators together. Run large models on consumer hardware.\n\nFull-Precision Inference\n\nNo quantization needed. Preserve the original model precision to ensure uncompromised inference quality.\n\nFull-Stack Inference & Fine-Tuning\n\nA complete local deployment toolchain from inference to fine-tuning, all-in-one for your development needs.\n\nMulti-Model Support\n\nSupports DeepSeek, Kimi, GLM, Qwen, MiniMax and more mainstream large models for diverse use cases.\n\nPowered by SGLang\n\nGPU inference powered by SGLang, combining strengths for outstanding inference performance.\n\nActive Community\n\nJoin thousands of users sharing benchmarks, configurations, and best practices.\n\n## Performance Highlights\n\nMiniMax-M2.1 FP8 full precision, single GPU benchmark (32K tokens input)\n\n2,540\n\nPrefill Speed (tokens/s)\n\n1x RTX 5090 (32GB) + 2x AMD EPYC 9355\n\n27.6\n\nDecode Speed (tokens/s)\n\n1x RTX 5090 (32GB) + 2x AMD EPYC 9355\n\n4.5x\n\nPrefill Speedup\n\nvs. llama.cpp (Q8_0 quantization)\n\n## Fine-Tuning Performance\n\nLow-VRAM full-parameter fine-tuning benchmarks\n\n--\n\nTraining Throughput (tokens/s)\n\nComing soon\n\n--\n\nVRAM Usage\n\nComing soon\n\n--\n\nvs. Full-GPU Training\n\nComing soon\n\n## Ready to get started?\n\nJoin the community and start running large models on your hardware today.", "url": "https://wpnews.pro/news/ktransformers-flexible-llm-inference-framework", "canonical_source": "https://ktransformers.net/en", "published_at": "2026-07-23 09:03:43+00:00", "updated_at": "2026-07-23 09:22:32.662123+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-infrastructure", "ai-tools", "developer-tools"], "entities": ["KTransformers", "RTX 5090", "AMD EPYC 9355", "MiniMax-M2.1", "SGLang", "llama.cpp", "DeepSeek", "GLM"], "alternates": {"html": "https://wpnews.pro/news/ktransformers-flexible-llm-inference-framework", "markdown": "https://wpnews.pro/news/ktransformers-flexible-llm-inference-framework.md", "text": "https://wpnews.pro/news/ktransformers-flexible-llm-inference-framework.txt", "jsonld": "https://wpnews.pro/news/ktransformers-flexible-llm-inference-framework.jsonld"}}