vLLM is an open-source inference engine that improves inference speed and memory efficiency so that large language models can serve many users simultaneously. Its signature technology is PagedAttention, a technique that manages GPU memory in page units to boost throughput. Rather than being a commercial product sold by a specific company, vLLM is an open-source project whose development and distribution are led by community maintainers. In August 2026, vLLM's lead maintainer shared operational experience running open models at a scale of 500,000 GPUs, offering a glimpse into large-scale inference infrastructure operations.
Current rank (1M)
25
Rank over the last 30 days
2026.08.20 – 2026.09.18 · High 16 · Low 25 · Now 25
Two or three units connect directly with QSFP cables, but four require a switch. NVIDIA's official documentation now includes an approved cable list and an automatic setup tool
vLLM-Omni and FastVideo's FastH3 have pushed generation time below the video's own runtime
Tencent says the model beat GPT-5.6 on a coding evaluation and even helped optimize its own training and inference process
GenRec converts viewing history into sentences instead of thousands of hand-crafted features, posting meaningful gains in online testing
A red-team tool checks everything from agent, skill, and MCP server vulnerabilities to LLM jailbreaks on a single platform
A Rust proxy that connects Claude Code and Codex to open-source models as-is — not yet production-ready
As surging Treasury yields threaten Asia's AI stock rally, AMD's next-generation server chip, a GPT-5.6 price cut, and open-source model and infrastructure discussions filled the day.
Alibaba's Qwen has opened up the full weights of its 27B multimodal model. At 4-bit precision it's 17.9GB — small enough to fit on a single graphics card — yet the official chart shows it beating Claude Opus 4.6 Max on several metrics.
At the Ai4 conference, three giants of the field offered differing takes on the risks of open-weight models and concerns over gatekeeping
LFM2.5-VL-3B hits 20 tokens/sec on Galaxy S26 Ultra, 228 tokens/sec on M5 Max
Muse Glimmer is an agent-specialized model that runs on 24GB-class consumer GPUs with 4-bit quantization
A lightweight model that turns policies into text-based questions, letting operators change content moderation standards without retraining
