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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesMeta and Broadcom announced an expanded, multi-year partnership on April 14, 2026, to develop multiple generations of Meta’s custom AI accelerators. The plan includes an initial commitment exceeding 1 gigawatt (GW) of compute capacity and a longer-term multi-gigawatt rollout. Those figures describe planned deployment, not capacity already installed or operating.
What did Meta and Broadcom announce?
The companies plan to work together on several generations of Meta Training and Inference Accelerator chips, or MTIA. Broadcom’s role spans custom-chip design, advanced packaging and Ethernet networking. Meta says the networking work is intended to connect its growing AI-compute clusters at high bandwidth.
Meta founder and CEO Mark Zuckerberg said the partnership would cover “chip design, packaging, and networking” as Meta builds computing infrastructure for its AI ambitions. Broadcom CEO Hock Tan described the expanded collaboration as supporting the next frontier of AI. Meta’s announcement said Tan would leave Meta’s board and become an advisor on the custom-silicon roadmap.
How much AI compute is planned?
The initial commitment is more than 1 GW, which the companies described as the first phase of a sustained multi-gigawatt rollout. Broadcom characterized the collaboration as multi-year and multi-generation, with plans extending through 2029. These are announced infrastructure plans; they do not establish that the full capacity has been delivered, installed or brought online.
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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
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- 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
A gigawatt is a measure of power, not a direct measure of chip count, computing performance or the amount of AI work a system can complete. The announcement does not provide enough detail to convert the planned power capacity into a comparable performance figure.
What is Meta’s MTIA chip?
MTIA is Meta’s family of custom accelerators for AI workloads across its apps and services. Meta says the chips are optimized for inference and recommendation workloads at scale. Inference is the process of running a trained model to produce results; it includes tasks such as generating responses and ranking content for users.
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- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
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- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
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- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
Meta’s strategy is not to make one accelerator serve every task. Its stated approach is to match different workloads with different chips, iterate quickly, prioritize inference and use established software and hardware ecosystems. The company has named PyTorch, vLLM, Triton and Open Compute Project standards among the technologies and standards relevant to its roadmap. These are Meta’s descriptions of its design strategy, not independently verified performance comparisons.
Where does the partnership fit in the MTIA roadmap?
Meta said it developed MTIA in 2023 and was developing and deploying four new generations within two years. Its March 2026 roadmap described the generations this way:
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- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
| Generation | Status or intended use in Meta’s March 2026 roadmap |
|---|---|
| MTIA 300 | Already in production for ranking and recommendations training, according to Meta. |
| MTIA 400 | Intended for broader workloads, with generative-AI inference production targeted for the near future. |
| MTIA 450 | Intended for broader workloads, with generative-AI inference production targeted for the near future and into 2027. |
| MTIA 500 | Intended for broader workloads, with generative-AI inference production targeted for the near future and into 2027. |
Meta said the newer chips’ modular design would allow them to fit existing rack infrastructure. The status and timing above reflect Meta’s roadmap statements, not independent confirmation that every intended milestone has been completed. Meta’s MTIA roadmap provides the company’s account of the generations and their intended workloads.
Is Meta replacing chips from other suppliers?
No. Meta describes its infrastructure strategy as a portfolio that combines its own custom silicon with chips and relationships from multiple industry partners. Its June 2026 infrastructure explainer names Broadcom, Arm, AWS, AMD and NVIDIA across custom development and supply relationships. Broadcom’s expanded role therefore strengthens Meta’s custom-chip effort without showing that the company is abandoning third-party accelerators.
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- 48GB AI graphics accelerator
In prepared remarks for Meta’s first-quarter 2026 results, Zuckerberg said the company was rolling out more than 1 GW of custom silicon being developed with Broadcom while also deploying significant AMD chips and new NVIDIA systems. Meta’s infrastructure explainer and its Q1 2026 results materials describe that broader mix.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What has not been established?
The announcements explain the partnership’s scope and planned scale, but they do not independently verify future deployment, savings or chip performance. The official materials do not provide an independently measured benchmark comparing MTIA’s performance or total cost with named competing chips. Claims about efficiency or advantages should therefore be understood as Meta’s own, rather than as independently established results.
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For readers assessing the business significance, the announcement signals planned investment in AI infrastructure and a broader custom-silicon relationship. It does not, on its own, establish the eventual cost, financial return or operational impact of that investment.
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