vLLM cuts memory use by loading PLE table rows on demand
A vLLM change reads only the required rows of a 51B model’s PLE table from safetensors files. On a DGX Spark GB10, loaded memory fell from 99.09 to 72.09 GiB, with decode about 3.5% slower at c=1.
By Wendelmaques Pereira · Updated today
Source: vLLM lê linhas PLE do Qwen dos safetensors para reduzir memória (x.com). Text prepared with AI from this source.
What happened and what to do
On November 1, 2026, the vLLM repository reported that the serving engine now reads only the necessary rows of a 51B Qwen model’s PLE table from the original safetensors files. On a DGX Spark GB10, loaded memory fell from 99.09 to 72.09 GiB; at c=1, decode speed remained within approximately 3.5% of its previous value. The change could make it easier to run large models with PLE on hardware with limited memory.
For a company, the opportunity is to test whether this approach enables larger models on available infrastructure without violating latency requirements. That calls for reproducible tests with the company’s own models, workloads, and hardware, along with memory and performance monitoring before and after the change. If the benefit is confirmed, the company can incorporate it into its inference service and track its behavior in production.
How the consultancy can help
Wendelmaques can diagnose the environment’s memory and performance limits, define an evaluation using representative workloads, and propose a scoped implementation. Work can include instrumentation, inference-service adjustments, and monitored operation.
Next step
Send a short description of your model, available hardware, and latency limits. Wendelmaques can assess the case and prepare a scoped proposal.
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I reply with scope and a proposal.