What TPU Economics Look Like in Practice
$4/hour vs $10/hour sounds great. But conversion cost, ecosystem limitations, and operational overhead change the math.
Blog
Deep dives into LLM inference optimization. Practical insights for developers and founders building with AI.
H100 vs A100: Which One for Inference
H100 costs 2x more than A100 but delivers 2x memory bandwidth. For decode-bound inference, that math matters.
Making the GPU vs TPU Decision
GPUs dominate LLM inference. TPUs offer interesting economics. Here's how to think about the choice.
Understanding What Makes vLLM Fast
vLLM serves 10x more requests than naive PyTorch. PagedAttention, continuous batching, and memory management make the difference.
Choosing Between vLLM SGLang and TensorRT
vLLM, SGLang, TensorRT-LLM—each optimizes for different things. Here's how to pick without running a 6-month bake-off.
The Math on Self-Hosting vs API
When does self-hosting break even? Here's the formula, the variables, and the 6-month reality check most teams skip.
When Self-Hosting Actually Saves Money
Everyone wants to self-host LLMs to save money. Most shouldn't. Here's the math on when it actually makes sense.
Designing Queues That Don't Explode
An unbounded queue is a memory leak waiting to happen. A too-small queue drops requests unnecessarily. Here's how to size and manage LLM request queues.
Tuning Batch Size for Your Workload
Batch size 1 wastes GPU. Batch size 64 kills latency. Somewhere in between is your sweet spot. Here's how to find it.
Implementing Request Priority in LLM Serving
Premium users expect faster responses. Batch jobs can wait. Here's how to implement priority queues that don't starve anyone.