A configuration mismatch in GLM 5.3 on Workers AI triggered an SGLang bug that exposed KV cache pages across requests. Here is how we addressed the issue.
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In this episode, Philip Kiely and Ali Taha from Baseten discuss the complexities and innovations in inference engineering for large AI models. They cover topics including model deployment, speculative decoding, quantization, hardware optimi…
Inference engineeringSpeculative decodingQuantizationModel deploymentTool callingKV cacheTensor parallelismExpert parallelismGPU hardwareRubin GPUVideo diffusionAutoregressive modelsDiffusion modelsTraining-inference convergenceContinual learningOpen source modelsInference infrastructureModel optimizationLatency vs throughputMulti-modal models
Reiner Pope delivers a blackboard lecture on the mathematical and hardware principles behind training and serving large language models. He explains how batch size, sparsity, and various parallelism strategies (expert, pipeline) impact late…
LLM trainingLLM inferenceBatch sizeLatency optimizationCost analysisRoofline analysisMemory bandwidthCompute performanceKV CacheSparsityMixture of ExpertsExpert parallelismData center architectureScale up networkScale out networkPipeline parallelismMicrobatchingMemory capacityChinchilla scalingRL generationAPI pricingContext lengthCryptographic ciphersNeural network architectureReversible networks