Dutch health AI startup Delphyr raises €1.75m - FinTech Global
Dutch health AI startup Delphyr raises €1.75m FinTech Global
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I wrote a fused MoE dispatch kernel in pure Triton that beats Megablocks on Mixtral and DeepSeek at inference batch sizes
Been working on custom Triton kernels for LLM inference for a while. My latest project: a fused MoE dispatch pipeline that handles the full forward pass in 5 kernel launches instead of 24+ in the naive approach. Results on Mixtral-8x7B (A100): Tokens vs PyTorch vs Megablocks 32 4.9x 131% 128 5.8x 124% 512 6.5x 89% At 32 and 128 tokens (where most inference serving actually happens), it's faster than Stanford's CUDA-optimized Megablocks. At 512+ Megablocks pulls ahead with its hand-tuned block-sparse matmul. The key trick is fusing the gate+up projection so both GEMMs share the same input tile from L2 cache, and the SiLU activation happens in registers without ever hitting global memory. Saves ~470MB of memory traffic per forward pass on Mixtral. Also tested on DeepSeek-V3 (256 experts) and

Design Cost-Optimized Compute Solutions
Exam Guide: Solutions Architect - Associate ⚡ Domain 4: Design Cost-Optimized Architectures 📘 Task Statement 4.2 🎯 Designing Compute Optimized Solutions is about choosing compute that meets performance and availability needs at the lowest reasonable cost . First decide what type of compute the workload needs (EC2, Lambda, Fargate, containers, edge, hybrid) , then choose how to pay for it , then right-size and scale it so you are not paying for idle capacity. You are balancing: 1 Performance 2 Availability 3 Elasticity 4 Operational Overhead 5 Purchasing Model Knowledge 1 | AWS Cost Management Service Features Cost Allocation Tags And Multi-Account Billing These help you understand and allocate compute cost. 1.1 Cost Allocation Tags Track compute spend by app, team, environment, owner, co
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