Unverified50% confidenceBenchmarkExact time
SkyRL's official report shows that by properly configuring expert adapters in vLLM, LoRA decoding throughput increased from 276 tok/s/GPU to 447 tok/s/GPU.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/17/2026
First Seen
Valid until: 1/15/2027
Sources
Related Entities
Related Claims
UnverifiedvLLM provides day-0 support for NVIDIA Vera Rubin NVL72, reporting 7.8x throughput per GPU versus GB200 NVL72 (AgentX) and 3.7x VLM throughput versus GB300 (MLPerf v6.1), with locality domains tuned for MoE routing.68% similarUnverifiedThe parallelism setting in vLLM must match the actual number of GPUs; setting GPU memory utilization too high causes OOM crashes, while setting it too low wastes hardware resources.66% similarUnverifiedvLLM's core innovation is the PagedAttention memory management mechanism, which can improve GPU memory utilization of the KV Cache from the conventional 40–60% to over 90%.66% similarUnverifiedOpen-source models such as LLaMA 3, Mistral, and Qwen 2.5 can already run smoothly on mainstream consumer-grade GPUs (such as the RTX 4090) at parameter scales ranging from 7B to 14B.66% similarUnverifiedAfter INT8 quantization, GLM-OCR can run inference on a single consumer-grade GPU (such as an RTX 3060/4060 with 12 GB VRAM) or even some mobile NPUs.65% similar
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