What is different about the hardware?
Groq describes compiler mapping and deterministic execution in its LPU architecture paper. Cerebras explains its SRAM-based approach in the inference architecture introduction. SambaNova documents operation mapping and memory tiers in its dataflow architecture overview.
The common question is how to keep computation supplied with data while controlling communication and scheduling overhead. These systems answer it differently. Their architecture descriptions alone do not provide a controlled comparison of application latency or cost.
Why can decode speed matter so much for agents?
An agent may make several sequential model calls, each followed by tool execution. Faster generation can reduce the model portion of every step. However, tool latency, queueing, input processing, retries, and correctness determine how quickly the whole task finishes. As a hypothetical example, reducing model time from two seconds to one second in each of five steps saves five seconds. If those steps also spend twenty seconds waiting on tools, the complete task falls from thirty seconds to twenty-five. Measure completed tasks and successful tool calls alongside tokens per second.Compare the service you can actually use
Check exact model IDs, context and output limits, structured-output support, tool calling, streaming behavior, and rate limits. Public and dedicated offerings can have different availability and capacity. Use the live Groq model catalog, Cerebras model selection guide, and SambaNova developer documentation. If the same checkpoint and serving precision are unavailable across services, describe the test as an application/provider comparison. It cannot isolate silicon performance. Avoid converting a vendor’s best-case token speed into a universal ranking.Hosted inference or infrastructure you operate?
An API trial can establish client compatibility and observed latency. It does not establish that you can run arbitrary containers, bring a private fine-tune, or deploy the hardware in your existing cloud account. Confirm those requirements with the specific offering. When comparing hosted inference with an AWS deployment, include the network round trip, data-handling requirements, capacity commitments, and fallback behavior. The benchmarking guide provides the common measurement framework. These vendor architectures are not additional Tensorfuse resource types.Related guides
Speculative decoding
Understand a software technique that also changes generation speed.
Compare hardware options
Evaluate specialized systems alongside GPUs and cloud accelerators.