Agent workloads may need different chips than standard GPUs
Based on 18 months of work on infrastructure for business AI agent deployments, NVIDIA GPUs look strong for training and normal chatbot inference, but less clearly suited to agent workloads. A comparison of SambaNova SN40L/SN50 with NVIDIA H200/B200 suggests that common GPU infrastructure is built more for producing large amounts of tokens cheaply in batches. That can work for chatbots, even if the speed per user is not very high.
Agents behave differently because they read long context, research, reason, call tools, read more, and then produce short bursts of output. A practical agent workload may have far more input than output, with an example ratio of 65 input tokens for every 1 output token. NVIDIA is still described as very strong at prompt processing, the step where the model reads the input before producing an answer.
SambaNova’s Reconfigurable Dataflow Unit is presented as a better fit for long, ordered agent workflows with many short completions.
Key points
- NVIDIA GPUs are described as strong for training and chatbot inference, but not always ideal for agents.
- Agent workloads often read a lot of context and produce only short outputs.
- The example workload uses a 65:1 input-to-output token ratio.
- NVIDIA is still described as excellent at prompt processing.
- SambaNova’s Reconfigurable Dataflow Unit is presented as a possible better match for agent workflows.
Quick term guide
- infrastructure
- The technical systems that keep a website or app running.
- Long Context
- The total amount of text or conversation history an AI can remember and process at once.
- input tokens
- Small pieces of text sent into an AI model as part of a request.
- prompt processing
- The step where the model reads the input before generating an answer.
- agent workflows
- Step-by-step work patterns where an AI agent handles a task.
- agent workflow
- A set of steps an AI follows automatically to complete a series of tasks in order.
- output tokens
- Small pieces of text produced by an AI model and used for pricing.
- context length
- The amount of text an AI model can read and work with at one time.