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Tagged “serving”

4 posts.

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All Model Architecture19 Training & Alignment22 Inference & Serving18 Agents & Orchestration11 Reasoning & Evaluation26 Safety, Security & Governance7 Platforms & Practice20
Inference & Serving 27 min

Everything Is Lossy Compression: A Rate-Distortion View of Quantisation, KV Caches, and Distillation

Weight quantisation, KV cache eviction, prompt compression and distillation are treated as four separate engineering disciplines with four separate literatures. They are one problem: choosing a point on a rate-distortion curve. S…

information-theory quantisation kv-cache distillation ∑ ◫
Inference & Serving 24 min

Goodput, Not Throughput: The Metric That Decides Whether Your LLM Deployment Works

Two servers run the same model on the same GPUs. One reports 4,200 tokens per second and is unusable; the other reports 2,600 and feels instant. Throughput is a property of the server, latency is a property of the request, and th…

inference serving latency goodput ∑ ◫
Platforms & Practice 22 min

LoRA Is Not Cheap Full Fine-Tuning: What Low-Rank Adaptation Actually Changes

LoRA is usually explained as a budget approximation of full fine-tuning: same destination, less memory. Two lines of evidence say that framing is wrong. LoRA learns less, forgets less, and reaches a structurally different solutio…

peft lora qlora fine-tuning ∑ ◫
Inference & Serving 24 min

Your Answer Depends on Who Else Is in the Batch

A thousand identical requests to the same model at temperature zero produced eighty different completions. The cause is not random seeds, not sampling, and not GPU atomics. It is that reduction kernels change their summation orde…

inference determinism numerics kernels ∑ ◫
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