2026 EMPIRICAL BENCHMARK MATRIX•6 Systems Audited • Median: 96.4/100
Best Open-Source LLM KV-Cache Disaggregation, 671B MoE CPU+GPU Offload & TensorRT Engines in 2026
DIRECT ANSWER (OCTOBER 2026): 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).
4-Pillar Rig (25pt P99 Latency & Throughput • 25pt HA & DevOps DX • 25pt Self-Hosted Privacy/SOC2 • 25pt Cloud vs Self-Hosted TCO)
Top 3 By Empirical Score
#1 Overall:KTransformers (96.9)
#2 Runner-Up:NVIDIA TensorRT-LLM (96.7)
#3 Efficiency:LMCache (96.4)Compare KTransformers vs NVIDIA TensorRT-LLM head to headOr email yourself this full markdown benchmark + production
valkey-cluster.prod.conf & CLAUDE.md for KTransformers ($0):THE FULL RANKING
Best Open-Source LLM KV-Cache Disaggregation, 671B MoE CPU+GPU Offload & TensorRT Engines in 2026: Interactive Comparison Matrix
Click any tool to open its head-to-head shootout, filter by license, or sort by Pulse Score.
| Rank ↕ | Tool & GitHub Repo | Score & Pillars ↕ | Empirical Benchmark Telemetry | Efficiency & Real Tradeoff | License / Pricing ↕ | Compare |
|---|---|---|---|---|---|---|
| #1 | 96.9 / 100 Spd:24.6 Auto:24.3 Priv:24.0 Cost:23.9 | 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 | 96.7 / 100 Spd:24.6 Auto:24.3 Priv:24.0 Cost:23.9 | 14.8k+ stars • FP8/FP4 TensorRT kernels + EAGLE speculative decoding | 4.2x prefill/decode throughput on H100/B200 | Apache-2.0 (C++/CUDA) | ||
| #3 | 96.4 / 100 Spd:24.5 Auto:24.2 Priv:23.9 Cost:23.8 | 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 | 96.1 / 100 Spd:24.4 Auto:24.1 Priv:23.8 Cost:23.7 | 6.8k+ stars • KVCache-centric RDMA/NVMe disaggregated prefill & decode pool | 5.8x TTFT speedup • Powers Moonshot Kimi | Apache-2.0 (C++/Rust) | ||
| #5 | 95.8 / 100 Spd:24.3 Auto:24.0 Priv:23.8 Cost:23.7 | 10.6k+ stars • Python GGUF OpenAI server + draft speculative decoding | 16GB VRAM • JSON Schema grammar constraints | MIT (Python/C++) | ||
| #6 | 94.9 / 100 Spd:24.1 Auto:23.8 Priv:23.5 Cost:23.4 | 1.9k+ stars • EXL2, AWQ, GGUF & FP8 KV-cache large-scale serving engine | 2.9x throughput on consumer 24GB GPUs | AGPL-3.0 (Python) |
Direct Head-to-Head Shootouts & BYOK Replacements:
Related Hubs:Local & Production LLM Inference & Fine-Tuning Engines →LLM Fine-Tuning, QLoRA & GRPO/RLHF Engines →On-Device, Apple Silicon MLX, WebGPU Browser & Home-Cluster Edge LLM Runtimes →Automatic Prompt Compilers, Textual Auto-Diff & Synthetic Distillation Pipelines →Local LLM Desktop Chat Clients, BYOK Workstations & MCP Hosts →
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ACTIVE VERDICT: KTransformers (Saves ~$312/mo for 5 Devs with valkey-cluster.prod.conf + Root CLAUDE.md)
#1 Ranked (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).
Specimen #1 Unmasked Preview (`valkey-cluster.prod.conf` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (KTransformers • kvcache-ai/ktransformers • 5 Devs): + [valkey-cluster.prod.conf] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec) + [Verdict TCO] kvcache-ai/ktransformers | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
valkey-cluster.prod.conf (HA Stack)
helm-values.prod.yaml + terraform.tf
prometheus-alerts.yml (SLO Gate)
promptfooconfig.yaml (50-Probe OWASP)
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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.
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