Day 0 support for Reflection’s Beam on Google Cloud: 5x faster perf with llm-d and NVIDIA Dynamo
Story summary
Today, Reflection released Beam, its first open-weight model built to give developers, researchers, and enterprises open control and customization for frontier software engineering and long-horizon agentic tasks. Released under a permissive Apache 2.0 license with BF16, FP8, and NVFP4 checkpoints av
📌 Key Highlights & Takeaways
- Today, Reflection released Beam, its first open-weight model built to give developers, researchers, and enterprises open control and customization for frontier software engineering and long-horizon agentic tasks.
- Released under a permissive Apache 2.0 license with BF16, FP8, and NVFP4 checkpoints av
Today, Reflection released Beam , its first open-weight model built to give developers, researchers, and enterprises open control and customization for frontier software engineering and long-horizon agentic tasks. Released under a permissive Apache 2.0 license with BF16 , FP8 , and NVFP4 checkpoints available at launch, Beam sets a new standard for inference efficiency in the 500B-parameter class by solving complex reasoning and coding problems with 3x to 4x higher reasoning compute efficiency than comparable open models.
Alongside today’s release, Google Cloud is announcing day 0 support and ready-to-run deployment recipes for serving Beam on Google Cloud AI Hypercomputer and NVIDIA AI Infrastructure.
To help developers get the full benefit of Beam’s token efficiency in production, we worked with Reflection to optimize performance of the underlying serving stack using llm-d (with vLLM) and NVIDIA Dynamo (with SGLang) on Google Cloud:
"By deploying our Beam model on Google Cloud, we achieved up to 5x higher performance and 8x lower latency to optimize serving of agentic workloads." - Joe Spisak, VP of Product & Head of Open Source, Reflection
With today’s day 0 release on Google Cloud, developers get:
Compounding model and system efficiency: Combining Beam’s 3x to 4x reasoning compute efficiency with system-level optimizations on A4X Max instances (NVIDIA GB300 NVL72) for up to 5x higher throughput and 8x lower latency,* and G4 VMs (NVIDIA RTX PRO 6000) for up to 2x higher throughput and 8x lower latency.*
High-efficiency multi-turn serving: KV-cache-aware routing, prefill/decode disaggregation, speculative decoding and hierarchical KV cache offloading to Host DRAM and Local SSD reduce redundant prompt recomputation across long coding sessions.
Turnkey optimized deployment: Our recommended performance optimized recipes for agentic workloads with llm-d and NVIDIA Dynamo are available in AI-Hypercomputer/gpu-recipes , llm-d repo and Model Garden in Gemini platform with a few clicks.
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