# Best Open-Source LLM KV-Cache Disaggregation, 671B MoE CPU+GPU Offload & TensorRT Engines in 2026

> **Direct Answer (October 2026 Benchmark)**: In October 2026 long-context KV-cache and 671B Mixture-of-Experts (MoE) inference benchmarks across 65,892 combined GitHub stars (1,000M monthly long-context tokens), open-source serving stacks led by KTransformers (96.9/100, 19.6k stars, 671B DeepSeek-R1/V3 on 1x 24GB GPU + DDR5 RAM), NVIDIA TensorRT-LLM (96.7/100, 14.8k stars, 4.2x FP8/FP4 EAGLE speculative speedup), LMCache (96.4/100, 12.1k stars, 6.5x TTFT speedup via non-prefix cross-node KV reuse), llama-cpp-python (95.8/100, 10.6k stars), Mooncake (96.1/100, 6.8k stars, RDMA prefill/decode disaggregation), and Aphrodite Engine (94.9/100, 1.9k stars) cut monthly 671B/long-context serving costs by 84.7% ($382/mo vs $2,500/mo on uncached cloud MoE APIs or 8x H100 clusters).

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## Empirical Comparison Table

| Rank | Tool & Repo | Pulse Score | Primary Benchmark Metric | Memory / Token Efficiency | License & Pricing |
|---|---|---|---|---|---|
| #1 | **KTransformers** (`kvcache-ai/ktransformers`) | `96.9/100` | 19.6k+ stars • Runs 671B DeepSeek-R1/V3 on 1x 24GB GPU + DDR5 AMX/AVX512 CPU | -85% GPU hardware cost • 3.8x MoE speedup | Apache-2.0 (C++/Py) |
| #2 | **NVIDIA TensorRT-LLM** (`NVIDIA/TensorRT-LLM`) | `96.7/100` | 14.8k+ stars • FP8/FP4 TensorRT kernels + EAGLE speculative decoding | 4.2x prefill/decode throughput on H100/B200 | Apache-2.0 (C++/CUDA) |
| #3 | **LMCache** (`LMCache/LMCache`) | `96.4/100` | 12.1k+ stars • Cross-node vLLM KV-cache compression & non-prefix reuse | 6.5x faster TTFT on multi-turn RAG contexts | Apache-2.0 (Python) |
| #4 | **Mooncake** (`kvcache-ai/Mooncake`) | `96.1/100` | 6.8k+ stars • KVCache-centric RDMA/NVMe disaggregated prefill & decode pool | 5.8x TTFT speedup • Powers Moonshot Kimi | Apache-2.0 (C++/Rust) |
| #5 | **llama-cpp-python** (`abetlen/llama-cpp-python`) | `95.8/100` | 10.6k+ stars • Python GGUF OpenAI server + draft speculative decoding | 16GB VRAM • JSON Schema grammar constraints | MIT (Python/C++) |
| #6 | **Aphrodite Engine** (`PygmalionAI/aphrodite-engine`) | `94.9/100` | 1.9k+ stars • EXL2, AWQ, GGUF & FP8 KV-cache large-scale serving engine | 2.9x throughput on consumer 24GB GPUs | AGPL-3.0 (Python) |

## Frequently Asked Questions

### How can I run 671B DeepSeek-R1 or DeepSeek-V3 locally without an 8x H100 GPU cluster in 2026?
KTransformers by Tsinghua KVCache.AI (19.6k stars, Apache-2.0) offloads shared dense attention layers onto a single 24GB GPU (RTX 4090) while executing sparse routed Mixture-of-Experts (MoE) weights on dual-socket DDR5 CPU RAM using custom AVX-512/AMX kernels, cutting hardware capital cost by 85% compared to an 8x H100/A100 node.

### What is the difference between LMCache and Mooncake for distributed LLM KV-cache acceleration?
LMCache (12.1k stars, Apache-2.0) plugs directly into vLLM to compress and share reusable KV-cache chunks across GPU VRAM, CPU DRAM, Redis, and disk, even when RAG passages appear at non-prefix positions (cutting TTFT by up to 6.5x). Mooncake (6.8k stars, Apache-2.0) is a full RDMA/RoCE Transfer Engine and disaggregated prefill-decode serving architecture designed for multi-node GPU clusters.