Introducing LLM Machines: the case for private inference
Meet LLM Machines: managed private inference for businesses that need control over model deployment, access, updates and ongoing operations.
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AI, in practice.
What changes in AI, what it enables, and what it takes to run it.
Models, hardware and inference, considered through the decisions European businesses need to make.
How LLM Machines delivers managed private inference
How LLM Machines sizes, configures and maintains private inference, with workload testing, documented APIs and agreed operating responsibilities.
Read articleTwo engines, one benchmark: what the DeepInfer comparison settles
SGLang matched DeepSeek's DeepInfer on DeepInfer's own benchmarks. What the comparison measures, where the two runs differ, and what it leaves out.
Read articleWhen a long prompt delays the short ones
A long prompt can slow the short requests behind it. What SGLang 0.5.22 changes in prefill and decode scheduling, and which latency number gets worse.
Read articleAn operating plan for private AI: models, access, updates and recovery
After the first successful request, a private AI service needs named owners, access rules with quotas, an accepted update and rollback path, a recovery target and a clear boundary around integration.
Read articleHow to design an inference benchmark that informs a purchase
A supplier benchmark can be real and still describe a different service. Define the workload, separate the first-token wait from generation, and agree the pass mark before you compare proposals.
Read articleWhat to check in an AI server beyond the chip headline
An AI server decision is not a chip decision. Check capacity, memory bandwidth, interconnect, precision support, power and availability against a named workload.
Read articleSGLang and prefix reuse: where an inference optimisation can help
Prefix reuse in SGLang: what a shared prompt prefix changes, which request patterns benefit, and the checks to run before you design a deployment around it.
Read articleFrom benchmark score to a private AI model shortlist
A benchmark score is a starting point. Five checks and a five-column worksheet turn a published result into a private AI model shortlist.
Read articleBefore you buy private AI infrastructure, define what you need to control
A practical checklist for private AI buyers: deployment location, operating authority, model updates, application ownership, acceptance, and capacity.
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