vllm
for ML engineers
Why this scored 0
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Outlook
low confidence · estimate, not a guarantee7-day
~0
range 0–12
14-day
~0
range 0–14
30-day
~0
range 0–19
Signal history
7-day window (free)projected trajectory (estimate, not a guarantee)
The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 145
Serving LLMs on Tenstorrent Hardware: Inside the vLLM TT Plugin
vLLM TT plugin enables efficient LLM serving on Tenstorrent hardware. · for ml engineers, infrastructure teams
lobstersEmerging 2 · vllmai2d ago - 239
Building and Serving vLLM with Rust
Demonstrates building and serving high-throughput vLLM inference pipelines using Rust for speed. · for backend engineers, ML infrastructure teams
devtoSteady 4 · rustdevtools26d ago - 331
Not Every LLM Needs vLLM: A Kubernetes Engineer's Guide to Serving Engines
Explains non-vLLM approaches to serve LLMs on Kubernetes, guiding ops and ML platform teams. · for devops engineers, ml platform teams
devtoSteady 2 · k8sai20d ago - 430
Self-hosting a lite agent backend on one TPU: Gemma 4 E2B + vLLM on a v5e-1
Run lite agent backend on one TPU · for devtools founders
devtoSteady 2 · vllmhardware32d ago - 529
Speculative Decoding in vLLM on AMD GPUs
Speculative decoding in vLLM can accelerate LLM inference on AMD GPUs. · for ML engineers
hackernewsSteady 2 · amdai3d ago - 625
The Complete Guide to Local LLM Inference Tools in July 2026: llama.cpp, Ollama, vLLM, SGLang, and Beyond
Local LLM tools compared · for ai devs, researchers
devtoEmergingai53d ago - 725
The Cheapest CUDA GPU on AWS Has an Arm CPU — and You Probably Want the Intel One
Cheapest AWS CUDA GPU uses Arm CPU, but Intel may be preferable for performance. · for ml ops
devtoCooling 2 · vllmai11d ago - 824
Serving Gemma4 with Rust on vLLM 🦀
Running Gemma4 on vLLM with Rust shows high-performance inference on AWS GPU instances. · for ML engineers
devtoCooling 2 · vllmai27d ago - 922
Comparing Open-Source LLM Gateways in 2026 to Run Enterprise AI at Scale
Compares open-source LLM gateways to help choose scalable enterprise inference stacks. · for ML platform teams
devtoCooling 2 · vllmai3d ago - 1020
Installing Rust for vLLM on Graviton: a G5g walk-through 🦀
Guide to installing Rust-based vLLM on Graviton G5g instances for cost-effective inference. · for ML infrastructure engineers
devtoSteadydevtools27d ago - 1118
Self-Hosting vLLM on Cloud GPUs in 2026: Sub-180ms LLM Inference for Autonomous AI Agents (Full Production Guide)
Guide details self-hosting vLLM on cloud GPUs for sub-180 ms LLM inference, supporting low-latency agents. · for ml engineers, platform teams
devtoSteadyai12d ago - 1216
vLLM v0.28.0
vLLM 0.28.0 improves LLM inference performance and resource usage. · for ml engineers, devops
hackernewsSteadyai11d ago - 1313
How vLLM Actually Manages KV Cache (vs the Toy Version I Built)
LLM cache management · for ai engineers
devtoSteady 3 · pythonai36d ago - 1410
vLLM for Baidu Kunlun
New hardware support · for hardware engineers
lobstersCoolinghardware42d ago - 1510
time-to-first-token — A 10-week, 30-minutes-a-day roadmap for LLM inference serving and optimization. vLLM, SGLang, quantization, speculativ
LLM optimization roadmap · for llm devs
githubSteady 2 · vllmai−16 saturated36d ago