vLLM
7 articles tagged with this topic
vLLM V1 Skews RL Results: Why Inference Correctness Beats Speed
Upgrading vLLM from V0 to V1 causes output inconsistencies in RL. If inference frameworks trade accuracy for speed, dependent models silently drift.
llama.cpp MTP Hits Beta: Local LLM Inference Speed Gap Narrowing
llama.cpp MTP beta supports Qwen3.5. With tensor parallelism maturing, the local-cloud inference speed gap is narrowing, making local LLM deployment m
Qwen 3.6 Wins Benchmarks, Fails Reality: Benchmaxing Distorts AI Perception
Qwen 3.6 won benchmarks but lost to Gemma 4 in practice, burning 8000+ tokens in a loop. Benchmaxing distorts AI perception; firms must shift to real-
Single 3090 Runs Qwen3 Natively on Windows: Local LLMs Drop Linux Requirement
Developers ran Qwen3.6-27B natively on Windows at 72 tok/s. This slashes deployment barriers—enterprises can run LLMs on existing GPUs without Linux.
Local LLMs Lose Tool Call Accuracy After 8–9 Chained Calls
Qwen 32B, Gemma 9B, and Command R 32B all fail similarly after 8+ tool calls — attention dilution, not context limits.
Running Gemma 4 26B-A4B on vLLM: Community Troubleshooting Notes
Developers report mixed results deploying Gemma 4 26B-A4B on vLLM, with INT4 quants too slow on DGX Spark GB10.
Agent Swarms + Continuous Batching Cut LLM Task Time 36x
Running 50 parallel agents on Qwen 27B drops a 42-minute research job to 70 seconds using continuous batching.