Groq vs SambaNova
September 28, 2025
Groq and SambaNova are both US-based startups that create hardware AI accelerators for large language models that improve performance and reduce power consumption compared to classic GPU accelerators (due to lower latency and increased throughput). Both companies offer full-stack solutions that include hardware (chips, systems) and software for running and deploying models.
But Groq (founded in 2016) produces a Language Processing Unit (LPU) chip that uses exclusively on-chip SRAM memory (without external DRAM/HBM modules), which requires installing multiple chips for scalable payloads. When working with very large models, Groq requires many more chips and racks than SambaNova, which increases power consumption and occupied space (but it secures higher speed). Its architecture minimizes elements associated with unpredictable behavior (branch prediction, caches, etc.). Groq shows better performance for small and medium batch/task sizes, especially if the model fits into their configuration.
SambaNova (2017) develops RDU (Reconfigurable Dataflow Unit) chip that uses a dataflow + memory combination of SRAM, HBM and external DRAM/DDR for greater flexibility and scalability. It can efficiently compute large model loads with fewer chips/racks than Groq, which reduces the power consumption. It supports 16-bit and mixed precision (bf16 / fp32) without sacrificing precision where possible. SambaNova supports Mixture of Experts and other methods that allow flexible separation of model, modules, memory management and computation when working with large volumes.
But Groq (founded in 2016) produces a Language Processing Unit (LPU) chip that uses exclusively on-chip SRAM memory (without external DRAM/HBM modules), which requires installing multiple chips for scalable payloads. When working with very large models, Groq requires many more chips and racks than SambaNova, which increases power consumption and occupied space (but it secures higher speed). Its architecture minimizes elements associated with unpredictable behavior (branch prediction, caches, etc.). Groq shows better performance for small and medium batch/task sizes, especially if the model fits into their configuration.
SambaNova (2017) develops RDU (Reconfigurable Dataflow Unit) chip that uses a dataflow + memory combination of SRAM, HBM and external DRAM/DDR for greater flexibility and scalability. It can efficiently compute large model loads with fewer chips/racks than Groq, which reduces the power consumption. It supports 16-bit and mixed precision (bf16 / fp32) without sacrificing precision where possible. SambaNova supports Mixture of Experts and other methods that allow flexible separation of model, modules, memory management and computation when working with large volumes.
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