Unless you've been sleeping in a cave, you've probably heard the buzz about OpenClaw. And if you've been scrolling Moltbook at all, you've definitely seen it.

OpenClaw is the open-source "doer" agent that's taking the developer world by storm – a locally hosted agent framework that doesn't just chat about ideas, but actually executes tasks directly from your machine. Moltbook is the place where agents congregate to discuss existential agent issues, swap tips on better ways to solve tasks, and debate the frailties of their human handlers!

OpenClaw is amazing, BUT... it’s heavy. In this guest post, the technical team at SambaNova introduces the “Agent Tax” problem and a new chip, the SN50 RDU, purpose-built to address the challenges of agentic inference. It’s insightful!

But to understand why the Agent Tax matters, it helps to first look at what makes an agentic framework like this fundamentally different and more demanding →

What Is an Agentic Workflow?

An agentic workflow is a loop where AI doesn't just respond to a prompt; it breaks a goal down into steps and executes them.

Even today, when you use ChatGPT or any large language model interface, OpenClaw creates a simplified version of this agentic loop to solve a user's request. The issue is that it requires a chain of individual calls to an LLM. For many use cases like coding, this introduces unacceptable latency with typical GPU configurations that impairs the developer experience.

The Problem: The "Agent Tax"

The biggest problem with autonomous agents is the ballooning token cost and time associated with running them. Unlike a simplex chatbot that answers one question, an agent like OpenClaw might enter a loop of Plan → Think → Act → Observe → Repeat Until Completion.

This input-heavy process burns through tokens rapidly. If you are routing all these steps through a massive, expensive proprietary model, like GPT-4 or Claude Sonnet, your bill explodes and your agent becomes sluggish. So much so that Anthropic introduced a Fast Mode option for Opus, which costs 6x more. And even that barely meets the minimum speed demands for agentic inference and for AI agents to deliver near-real-time answers.

Consider a complex OpenClaw task that requires 10 autonomous actions (searching, coding, testing, debugging). If each inference step takes 30 seconds on a traditional cloud:

Speed = Intelligence

On fast infrastructure, speed isn't just about saving time – it's about increasing the quality of the output. When inference is near-instant and ultra-cheap, your agent can afford to be "thorough." It can:

By removing the "latency tax," the right infrastructure turns a frustrating experiment into a seamless, "always-on" utility.

The Solution: An Optimized Agentic Workflow Cloud

The future of efficient agents isn't "one giant model to rule them all."

It's a combination of sub-agents powered by different specialized models working together, where the fidelity of the model matches the complexity of the task. You don't need a sledgehammer to crack a nut; you can use cheaper open-source models and a combination of smaller models – like MiniMax, DeepSeek, gpt-oss, and Qwen – all working together to achieve the same goal with much higher efficiency.

By using an AI infrastructure optimized for agentic workflows, you can leverage a high-intelligence model for complex planning while deploying smaller, faster open-source models for targeted sub-tasks.

The challenge is that delivering this kind of architecture at the speeds agentic workflows demand becomes prohibitively expensive or unscalable with conventional hardware. That's the problem SambaNova has been working to solve since the company's founding nearly a decade ago – and this week, that work reached a new milestone.

Introducing the SN50 RDU: Purpose-Built for Agentic Inference

At its core, AI inference is a data and memory movement challenge. When you don't solve this data movement problem efficiently, the industry challenges of energy, latency, and cost are unavoidable. This insight was a key driver among the founding principles of SambaNova, and it resulted in the development of the Reconfigurable Dataflow Unit (RDU) – a fundamentally different hardware architecture for AI.

SambaNova recently announced its fifth-generation chip: the SN50 RDU, and the SambaRack SN50 system. Both are purpose-built to solve the challenge of agentic inference unlike any other platform.

Tokenomics That Make Sense for Agents

The SN50 RDU delivers an unmatched blend of ultra-low latency, high throughput, and power-efficient performance for AI inference workloads, fundamentally reshaping the economics of token generation.

Compared to Blackwell B200 GPUs, the SN50 delivers 5x the maximum speed and over 3x the throughput for agentic inference across a range of models. On Meta's Llama 3.3 70B – a widely-used open-source model – the performance gap is consistent and significant. For larger models like gpt-oss 120B, the TCO advantage reaches 8x the savings compared to B200 GPU deployments.

This impressive performance is delivered while averaging just 20 kW of power in a SambaRack, which allows the rack to operate in existing air-cooled data centers – no specialized infrastructure required.

Agentic Caching

Just like the SN40L RDU, the SN50 features a tiered memory architecture that combines large-capacity memory, high-bandwidth memory (HBM), and ultra-fast SRAM. This hierarchy enables the chip to host the largest models while simultaneously running many models in parallel.

Models residing in HBM and SRAM can be hot-swapped in milliseconds – a capability that is essential for agentic workloads switching frequently between multiple models. With the SN50, input tokens can also be cached in memory, reducing pre-fill processing time and the Time to First Token (TTFT) for requests.

In combination, this memory architecture becomes ideal for agents: an agentic cache that can process tasks even more efficiently, making it perfectly suited to the patterns of model-switching and context reuse that define multi-agent workflows.

Next-Generation Scale Out

The SambaRack SN50 combines 16 SN50 chips together to deliver five times more compute per accelerator and four times more network bandwidth than the previous generation.

Interconnected SambaRacks can scale up to 256 accelerators over a multi-terabyte-per-second interconnect, which cuts TTFT and supports larger batch sizes. The system supports individual models up to 10 trillion parameters in size and context lengths of up to 10 million tokens – built not just for the models of today, but for the trajectory the field is clearly on.

Dataflow Architecture: Why It Works

At the heart of the SN50 and SN40 RDUs is the dataflow architecture, which enables high performance and high efficiency.

While GPUs are good at AI model training – a compute-heavy function – AI inference is fundamentally a data movement and memory optimization challenge that requires a different architectural approach. To perform AI inference, GPUs must make multiple, redundant calls to off-chip HBM memory. Each memory call adds latency and consumes energy, which is why GPUs demand so much power.

RDUs instead map the graph of a given AI model to the most efficient path for moving data across the processor. This approach eliminates redundant memory calls, which drastically reduces latency and power consumption – and it's why SambaNova has been able to deliver the speed and efficiency profile that agentic workloads actually require. If a GPU is workshop that does heavy compute for each operation, then a RDU is an assembly line that passes data seamlessly from one operation to the next. 

The SambaNova Advantage for OpenClaw

Bringing this back to where we started: why is SambaNova uniquely suited for agentic workflows like OpenClaw?

It starts with the hardware architecture. The next generation of complex agentic systems demands ultra-low latency, high throughput, the ability to handle unpredictable bursty workloads, infrastructure capable of serving multiple models simultaneously, and massive memory for caching multiple models and prompt context. The SambaRack with its RDU chip is purpose-built for this future.

Ready to take the brakes off your OpenClaw agent? You can try OpenClaw with any of SambaNova's models today – including the newly released MiniMax 2.5 on SambaCloud here. This model is running on SambaNova’s SN40L RDUs and even on their fourth generation chip, they are one of the fastest service providers for that model, which you can try today.

Want to learn more about how to build inference services with this chip in your data center? Contact the SambaNova team to learn more about SambaStack, which supports both their fourth and fifth generation chips. Get started now with SN40L as the SN50 RDU and SambaRack SN50 system will begin shipping to customers in the second half of 2026.

*This guest post was written by Abhi Ingle, Chief Product and Strategy Officer at SambaNova Systems. We thank SambaNova for their support of Turing Post’s mission to bring clarity to the AI landscape.