GPUYard

 Deploying multi-agent workflows in production is a memory-bandwidth problem, not a compute problem. When engineering teams transition from prototyping simple Python scripts to running continuous, autonomous agent loops, they inevitably hit a wall. Frameworks like Microsoft AutoGen rely on agents continuously passing context back and forth. If you push this through public cloud APIs, the exponential prompt expansion will ruin your budget. The $14,000 Cloud API Problem We recently analyzed a mid-sized FinTech company trying to automate compliance auditing with a 5-agent AutoGen setup using a popular pay-as-you-go cloud API. Within three weeks, their infrastructure bill crossed $14,000 . Every time agents iteratively self-corrected, the entire conversation history was sent back through the API, burning millions of tokens per hour and hitting strict Tokens-Per-Minute (TPM) limits. The Solution: AutoGen + vLLM on Bare-Metal A100s By migrating the exact same AutoGen logic to a single ...

  Most AI infrastructure teams are quietly wasting a massive share of their GPU budget. If you are running your Large Language Model (LLM) inference stack by pushing every request through a single, shared GPU pool, you are experiencing the "Monolithic Inference Bottleneck." In a standard setup, one model instance handles the entire lifecycle of a prompt. But the two phases of inference behave completely differently: The Prefill Phase (Compute-Bound): The model processes the input prompt in parallel. This requires massive compute power (large matrix multiplications) and determines your Time to First Token (TTFT). The Decode Phase (Memory-Bound): The model generates output tokens autoregressively. It constantly reads the KV cache, making it heavily dependent on memory bandwidth. When both phases share the same GPU, a long prefill burst will stall decode steps (spiking latency for current users), while a batch full of decode steps leaves your expensive compute units under-util...