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LMSYS Blog

实时热榜 · 科技

1 个榜单5小时前更新默认榜单

LMSYS Blog · 实时热榜

5小时前更新

01
Scaling JEV-like Decision Models with SGLang - LMSYS Org
Scaling JEV-like Decision Models with SGLang LMSYS OrgLMSYS Org
02
Accelerating Long-Context and Agentic Inference with NVFP4 KV Cache - LMSYS Org
Accelerating Long-Context and Agentic Inference with NVFP4 KV Cache LMSYS OrgLMSYS Org
03
SGLang and Miles Add Day-0 Support for DeepSeek-V4.1 - LMSYS Org
SGLang and Miles Add Day-0 Support for DeepSeek-V4.1 LMSYS OrgLMSYS Org
04
MiniMax-H3 on 8×H200: 1.95× Lossless, Up to 6.24× at 0.76–0.91 SSIM - LMSYS Org
MiniMax-H3 on 8×H200: 1.95× Lossless, Up to 6.24× at 0.76–0.91 SSIM LMSYS OrgLMSYS Org
05
Qwen3.8-Flash-Next: Day-0 Support in SGLang - LMSYS Org
Qwen3.8-Flash-Next: Day-0 Support in SGLang LMSYS OrgLMSYS Org
06
Mooncake for Miles: From Fragmented Rollout Data to Efficient Bulk I/O - LMSYS Org
Mooncake for Miles: From Fragmented Rollout Data to Efficient Bulk I/O LMSYS OrgLMSYS Org
07
Pushing the Limits of Serving DeepSeek-V4-Pro - LMSYS Org
Pushing the Limits of Serving DeepSeek-V4-Pro LMSYS OrgLMSYS Org
08
Miles v0.1: Production-level Post-training - LMSYS Org
Miles v0.1: Production-level Post-training LMSYS OrgLMSYS Org
09
Advanced CUDA Graph Techniques in SGLang - LMSYS Org
Advanced CUDA Graph Techniques in SGLang LMSYS OrgLMSYS Org
10
Unified Radix Cache: One Tree for Hybrid Model Prefix Caching - LMSYS Org
Unified Radix Cache: One Tree for Hybrid Model Prefix Caching LMSYS OrgLMSYS Org
11
SGLang Adds Day-0 Support for NVIDIA Nemotron 3.5 Lightning - LMSYS Org
SGLang Adds Day-0 Support for NVIDIA Nemotron 3.5 Lightning LMSYS OrgLMSYS Org
12
SGLang Adds Day-0 Support for Muse Glimmer, a Multimodal Model Built for Local Agentic Workflows - LMSYS Org
SGLang Adds Day-0 Support for Muse Glimmer, a Multimodal Model Built for Local Agentic Workflows LMSYS OrgLMSYS Org
13
HPC-Ops × SGLang: High-Performance Attention, Router GEMM, and MoE Kernels from Tencent Hunyuan - LMSYS Org
HPC-Ops × SGLang: High-Performance Attention, Router GEMM, and MoE Kernels from Tencent Hunyuan LMSYS OrgLMSYS Org
14
SpecForge v0.3.0: a Unified Disaggregated and Colocated Speculative Decoding Stack, and New Open SpecBundle Draft Models - LMSYS Org
SpecForge v0.3.0: a Unified Disaggregated and Colocated Speculative Decoding Stack, and New Open SpecBundle Draft Models LMSYS OrgLMSYS Org
15
RadixArk Joins Forces with Google to Bring Full SGLang Features to TPUs - LMSYS Org
RadixArk Joins Forces with Google to Bring Full SGLang Features to TPUs LMSYS OrgLMSYS Org
16
Toward a Cleaner Quantization Stack in SGLang - LMSYS Org
Toward a Cleaner Quantization Stack in SGLang LMSYS OrgLMSYS Org
17
SGLang and Miles Add Day-0 Support for Kimi K3 - LMSYS Org
SGLang and Miles Add Day-0 Support for Kimi K3 LMSYS OrgLMSYS Org
18
OPD Support in Miles - LMSYS Org
OPD Support in Miles LMSYS OrgLMSYS Org
19
Serving GLM5.2 NVFP4 Agentic Workload with SGLang: Reaching 500 TPS in 2 Weeks - LMSYS Org
Serving GLM5.2 NVFP4 Agentic Workload with SGLang: Reaching 500 TPS in 2 Weeks LMSYS OrgLMSYS Org
20
Accelerating SGLang HiCache with Netpreme X-Mem™ MPU - LMSYS Org
Accelerating SGLang HiCache with Netpreme X-Mem™ MPU LMSYS OrgLMSYS Org
21
DSpark in SGLang: Speculative Decoding with Confidence-Driven, Variable-Length Verification - LMSYS Org
DSpark in SGLang: Speculative Decoding with Confidence-Driven, Variable-Length Verification LMSYS OrgLMSYS Org
22
Agent-Assisted SGLang Development: An Initial Exploration - LMSYS Org
Agent-Assisted SGLang Development: An Initial Exploration LMSYS OrgLMSYS Org
23
Improving DeepEP MoE Load Balance in SGLang with Waterfill and LPLB - LMSYS Org
Improving DeepEP MoE Load Balance in SGLang with Waterfill and LPLB LMSYS OrgLMSYS Org
24
MOSS-TTS Local Transformer v1.5 on SGLang-Omni: Serving Native-Streaming 48 kHz Speech - LMSYS Org
MOSS-TTS Local Transformer v1.5 on SGLang-Omni: Serving Native-Streaming 48 kHz Speech LMSYS OrgLMSYS Org
25
The next generation of speculative decoding: DFlash and Spec V2 - LMSYS Org
The next generation of speculative decoding: DFlash and Spec V2 LMSYS OrgLMSYS Org
26
Higgs Audio v3 TTS on SGLang-Omni: Real-Time, Controllable Speech for Voice Agents - LMSYS Org
Higgs Audio v3 TTS on SGLang-Omni: Real-Time, Controllable Speech for Voice Agents LMSYS OrgLMSYS Org
27
SGLang and Miles Add Day-0 Support for NVIDIA Nemotron 3 Ultra for Long-Running Autonomous Agents - LMSYS Org
SGLang and Miles Add Day-0 Support for NVIDIA Nemotron 3 Ultra for Long-Running Autonomous Agents LMSYS OrgLMSYS Org
28
Heterogeneous CPU + GPU EPD Disaggregation to Boost VLM Serving - LMSYS Org
Heterogeneous CPU + GPU EPD Disaggregation to Boost VLM Serving LMSYS OrgLMSYS Org
29
Win on TCO: How AMD Instinct™ MI355X Achieves Cost-Competitive Distributed Inference Through SGLang with MoRI - LMSYS Org
Win on TCO: How AMD Instinct™ MI355X Achieves Cost-Competitive Distributed Inference Through SGLang with MoRI LMSYS OrgLMSYS Org
30
Updating 1T parameters in seconds — P2P weight transfer in Large Scale Distributed RL - LMSYS Org
Updating 1T parameters in seconds — P2P weight transfer in Large Scale Distributed RL LMSYS OrgLMSYS Org
31
DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles - LMSYS Org
DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles LMSYS OrgLMSYS Org
32
HiSparse: Turbocharging Sparse Attention with Hierarchical Memory - LMSYS Org
HiSparse: Turbocharging Sparse Attention with Hierarchical Memory LMSYS OrgLMSYS Org
33
Highlights of SGLang at NVIDIA GTC 2026 - LMSYS Org
Highlights of SGLang at NVIDIA GTC 2026 LMSYS OrgLMSYS Org
34
Elastic EP in SGLang: Achieving Partial Failure Tolerance for DeepSeek MoE Deployments - LMSYS Org
Elastic EP in SGLang: Achieving Partial Failure Tolerance for DeepSeek MoE Deployments LMSYS OrgLMSYS Org
35
ROCm Support for Miles: Large-Scale RL Post-Training on AMD Instinct™ GPUs - LMSYS Org
ROCm Support for Miles: Large-Scale RL Post-Training on AMD Instinct™ GPUs LMSYS OrgLMSYS Org
36
Unlocking 25x Inference Performance with SGLang on NVIDIA GB300 NVL72 - LMSYS Org
Unlocking 25x Inference Performance with SGLang on NVIDIA GB300 NVL72 LMSYS OrgLMSYS Org
37
Deploying DeepSeek on GB300 NVL72: Big Wins in Long-Context Inference - LMSYS Org
Deploying DeepSeek on GB300 NVL72: Big Wins in Long-Context Inference LMSYS OrgLMSYS Org
38
Unleashing Computational Power: Ultimate Latency Optimization of Qwen3 and Qwen3-VL on AMD MI300X Series - LMSYS Org
Unleashing Computational Power: Ultimate Latency Optimization of Qwen3 and Qwen3-VL on AMD MI300X Series LMSYS OrgLMSYS Org
39
Squeezing 1TB Model Rollout into a Single H200: INT4 QAT RL End-to-End Practice - LMSYS Org
Squeezing 1TB Model Rollout into a Single H200: INT4 QAT RL End-to-End Practice LMSYS OrgLMSYS Org
40
Optimizing GLM4-MoE for Production: 65% Faster TTFT with SGLang - LMSYS Org
Optimizing GLM4-MoE for Production: 65% Faster TTFT with SGLang LMSYS OrgLMSYS Org
41
SGLang-Diffusion: Two Months In - LMSYS Org
SGLang-Diffusion: Two Months In LMSYS OrgLMSYS Org
42
Pipeline Parallelism in SGLang: Scaling to Million-Token Contexts and Beyond - LMSYS Org
Pipeline Parallelism in SGLang: Scaling to Million-Token Contexts and Beyond LMSYS OrgLMSYS Org
43
EPD Disaggregation: Elastic Encoder Scaling for Vision-Language Models in SGLang - LMSYS Org
EPD Disaggregation: Elastic Encoder Scaling for Vision-Language Models in SGLang LMSYS OrgLMSYS Org
44
Power Up Diffusion LLMs: Day‑0 Support for LLaDA 2.0 - LMSYS Org
Power Up Diffusion LLMs: Day‑0 Support for LLaDA 2.0 LMSYS OrgLMSYS Org
45
Mini-SGLang: Efficient Inference Engine in a Nutshell - LMSYS Org
Mini-SGLang: Efficient Inference Engine in a Nutshell LMSYS OrgLMSYS Org
46
SGLang Day-0 Support for MiMo-V2-Flash Model - LMSYS Org
SGLang Day-0 Support for MiMo-V2-Flash Model LMSYS OrgLMSYS Org
47
SGLang Adds Day-0 Support for the Highly Efficient, Open Nemotron 3 Nano Hybrid MoE Model - LMSYS Org
SGLang Adds Day-0 Support for the Highly Efficient, Open Nemotron 3 Nano Hybrid MoE Model LMSYS OrgLMSYS Org
48
Let Tensors Fly — Accelerating Large Model Weight Loading with R-Fork - LMSYS Org
Let Tensors Fly — Accelerating Large Model Weight Loading with R-Fork LMSYS OrgLMSYS Org
49
Boost SGLang Inference: Native NVIDIA Model Optimizer Integration for Seamless Quantization and Deployment - LMSYS Org
Boost SGLang Inference: Native NVIDIA Model Optimizer Integration for Seamless Quantization and Deployment LMSYS OrgLMSYS Org
50
From research to production: Accelerate OSS LLM with EAGLE-3 on Vertex - LMSYS Org
From research to production: Accelerate OSS LLM with EAGLE-3 on Vertex LMSYS OrgLMSYS Org
51
Unified FP8: Moving Beyond Mixed Precision for Stable and Accelerated MoE RL - LMSYS Org
Unified FP8: Moving Beyond Mixed Precision for Stable and Accelerated MoE RL LMSYS OrgLMSYS Org

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