You can avoid building an AI system around Broadcom by renting cloud capacity built on a provider’s custom accelerators, using cloud GPUs, or commissioning custom or semi-custom infrastructure through another partner. These options differ in software compatibility, capacity, deployment control, engineering effort and supply chain. Replacing the accelerator designer alone does not guarantee that Broadcom is absent from the rest of the system.
First, define what “without Broadcom” means
Broadcom can be involved in different parts of an AI infrastructure project. A buyer might mean a different accelerator designer, a different networking supplier, a different system integrator—or verified exclusion of Broadcom from the entire bill of materials. Those are distinct requirements. A cloud service can spare an organization from designing and operating its own accelerator racks, but it does not by itself establish which companies supplied every component behind the service.
For many organizations, the practical alternative to building is to consume infrastructure as a cloud service. Custom silicon or a partner-built rack is more relevant to hyperscalers and other organizations with the scale, engineering resources and procurement capacity to take on a system-level project.
Cloud-hosted custom accelerators
Cloud accelerators let a team use a provider’s chip and infrastructure without owning the physical cluster. They are not automatically drop-in replacements for GPUs: supported models, frameworks, operators, compilation, optimization and network behavior can all affect whether a workload ports cleanly.
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
AWS Trainium and Inferentia
AWS positions Trainium for training and inference, and Inferentia for inference, through its cloud services and software. AWS also offers GPU infrastructure, so choosing AWS does not require choosing only its custom chips. A 2025 UK Competition and Markets Authority (CMA) report described AWS chips as available to cloud customers; that market-structure finding is not a statement about current instance availability in every region.
AWS’s undated Trainium research page, accessed October 3, 2026, describes a $110 million Build on Trainium research and education investment program and a dedicated research cluster with capacity for up to 40,000 Trainium chips. These are AWS-published program and cluster-capacity figures, not chip prices, independent performance results or a promise of customer capacity.
Google Cloud TPU
Google describes its TPUs as custom accelerators for training, tuning and deployment, with support listed for PyTorch, JAX and vLLM. Its cloud-service model is a candidate for teams that want access to accelerator infrastructure without operating an owned physical cluster. Verify that the specific TPU capacity, framework features and service terms you need are available for your project.
Rank #2
- Powered by Radeon AI PRO R9700 - Supercharge you workflow with the cutting-edge RDNA 4 Architecture and 2nd-gen AI Accelerators.
- 32GB GDDR6 with 256-bit memory bus - Tackle larger, more complex projects without limits.
- PCIe Gen 5 - Unlock lightning-fast data transfers with PCIe Gen 5 support.
- GIGABYTE TURBO Fan Cooling System - Indented metal cover and blower fan increase airflow intake, while the vapor chamber, all copper heat sink, and metal frame offer efficient heat dissipation. Optimized airflow design allows for easy multi-GPU scalability.
- Double Ball Bearing Fan - Delivers superior heat resistance and rotational efficiency for better performance and a longer lifespan compared to conventional sleeve fans.
Microsoft Maia
Microsoft announced Maia 200 on January 26, 2026, as an inference accelerator. Microsoft said it was deployed in the US Central Azure region and described an Azure-integrated software and networking stack. Confirm current service access, eligible workloads and capacity before treating Maia as an available option for a particular deployment.
Microsoft specifies 216 GB of HBM3e memory at 7 TB/s for Maia 200. It also claims 30% better performance per dollar than the latest-generation hardware then in its fleet. Microsoft further claimed Maia 200’s FP4 performance was three times that of third-generation Amazon Trainium and its FP8 performance exceeded Google’s seventh-generation TPU. These are Microsoft’s specifications and comparisons, not independent cross-provider tests; they should not be generalized beyond the stated comparison or assumed to predict results for a different model, precision or workload.
Cloud GPUs or a mix of accelerator types
Cloud GPUs can be a sensible route when a workload depends on existing GPU-based tools or when flexibility across software and workloads matters. AWS describes both GPU instances and Trainium-based infrastructure. Its August 2026 announcement also described planned support for NVIDIA GPU and Trainium systems, including NVLink Fusion integration into next-generation Trainium infrastructure. That announcement indicates an intended mix of accelerator paths; it does not establish that every announced system is currently offered in every region.
Rank #3
- [Local AI Inference & 70B Model Ready] Equipped with the AMD Ryzen 7 PRO 8845HS processor, NEXUS is engineered for heavy local AI workloads. With a full-size GPU bay, it runs 70B LLMs natively without an internet connection. Ideal for AI developers and tech enthusiasts who need private environment for coding and model testing.
- [132TB Mass Storage with ZFS Integrity] Features a hybrid storage architecture (3×NVMe + 4×3.5" HDD) supporting up to 132TB. Utilizing the enterprise-grade ZFS file system and ECC memory, it prevents data corruption and bit rot—a must-have for professional photographers and video editors safeguarding 4K/8K RAW footage.
- [OpenClaw-Driven Automation Workflow] The built-in OpenClaw execution layer allows complex automated tasks to be processed locally. Even when offline, your backup schedules and AI file organization continue seamlessly. Say goodbye to monthly cloud subscriptions and high latency.
- [Dual 10GbE & USB4 Ultra-Connectivity] Experience server-class speeds with dual 10GbE ports and a 40Gbps USB4 interface. It enables multi-user real-time collaboration on large project files directly from the NAS, ensuring zero-lag editing for creative studios and production teams.
- [Open-Source ZimaOS for Total Privacy] Running on the fully open-source ZimaOS, NEXUS ensures your data stays physically on-premise with no backdoors. It acts as a "Digital Fortress" for privacy-conscious families and small businesses who demand absolute data sovereignty.
The available evidence does not establish a neutral NVIDIA-versus-AMD hardware or price comparison, or a universal GPU winner. Evaluate the instance types and accelerators actually available to your account and region, using the workloads you intend to run.
Custom or semi-custom infrastructure through another partner
For organizations building at very large scale, NVIDIA and Marvell announced a partnership in which Marvell will provide custom XPUs and NVLink Fusion-compatible scale-up networking within a rack-scale platform. This is a potential route for a large infrastructure builder seeking custom silicon within a specified interconnect ecosystem. The announcement does not make the platform a ready-made purchase for an ordinary enterprise, establish its fit for a particular buyer, or disclose every supplier involved in a resulting deployment.
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Custom silicon is a system project, not just a chip order. The CMA’s 2025 decision noted the software needed to program custom accelerators and the investment required to develop it. Networking, systems integration, deployment and supply coordination also affect whether a design can be used effectively. Assess engineering and operational requirements alongside chip specifications.
Rank #4
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
Does choosing another accelerator remove Broadcom from the system?
Not necessarily. In an October 13, 2025 announcement, OpenAI said it would design accelerators developed and deployed in partnership with Broadcom, and described racks using Broadcom Ethernet and other connectivity solutions. This example shows that customer-designed accelerators and Broadcom components can coexist in one system. It does not establish that every alternative provider uses Broadcom.
If exclusion is a procurement requirement, specify it at the component and supplier level. Ask vendors to identify the companies responsible for accelerator design and manufacture, packaging, networking, NICs, switches, optics and rack integration, and define what evidence will verify compliance. A different cloud provider or accelerator brand alone is not proof of a Broadcom-free supply chain.
Compare options against the actual workload
| Decision area | What to establish |
|---|---|
| Workload | Is the priority pretraining, fine-tuning, inference or a mix? Do the accelerator’s model support, memory, precision and parallelism suit the workload? |
| Software portability | Which frameworks, operators, compilers, kernels and inference engines are supported? What must be ported, rewritten or optimized? |
| Network and scale | Which scale-up and scale-out links, collective operations, storage and cluster topology are included, and do they suit the target scale? |
| Capacity and access | Is the required capacity available in the intended region and deployment window? Is the system generally available, limited, planned or otherwise subject to access conditions? |
| Full cost | Compare accelerator time, networking, storage, utilization, engineering and migration. For owned infrastructure, include power and cooling. Test vendor performance or cost claims on a representative workload. |
| Control and location | Is cloud operation acceptable, or does the project require owned or dedicated infrastructure, a specific data location or greater operational control? |
| Supply chain | Which companies design, manufacture, package, connect and supply the accelerator, NICs, switches, optics and rack system? What exactly does “without Broadcom” require? |
There is no neutral cross-vendor benchmark or end-to-end cost comparison established for these choices here. Vendor-reported performance and efficiency can depend on the model, precision, workload, utilization, region and software stack. A meaningful procurement comparison therefore needs to use the same representative workload and operating assumptions, rather than relying on headline claims alone.
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A practical evaluation sequence
- Write down the requirement. Decide whether the goal is to avoid owning infrastructure, use a particular accelerator or framework, secure dedicated capacity, or exclude Broadcom from specified components.
- Shortlist by workload and deployment model. Compare provider-hosted accelerators and GPUs for cloud use; consider a custom or semi-custom partner only if the organization can take on the system and software work.
- Check service access before testing. Ask each provider about the required region, capacity, service status, eligible workloads and deployment timing. Treat announcements and planned integrations as different from confirmed access.
- Port and benchmark a representative workload. Include the actual model, precision, software dependencies and serving or training pattern. Record engineering effort and operational constraints as well as runtime performance.
- Build a like-for-like total-cost estimate. Include migration and utilization assumptions, and include networking, storage, power and cooling where applicable. Do not use one vendor’s performance-per-dollar claim as a cross-vendor conclusion.
- Verify the supply chain if exclusion matters. Make the requirement explicit in procurement documents and request component-level supplier information and verification, not just a different accelerator or cloud brand.
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