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• Some intelligence agencies such as Bloomberg projects the AI accelerator market could reach about $604 billion by 2033, growing at roughly 16% CAGR.
• Other analysts differ in methodology but all show rapid expansion such as Mordor Intelligence at ~$440 billion by 2030 or Fortune BI ~$309 billion by 2034. The exact number varies because definitions differ (GPU-only vs. all accelerators vs. full AI infrastructure), but the direction is consistent: multi-hundred-billion-dollar market this decade.
• Although GPU is leading with a 60% market share, ASICs are projected to grow at almost 30% CAGR within the 2026-2030 period.
• Drivers behind this significant growth:

• GPUs remain dominant: GPUs still account for the majority of accelerator revenue and are expected to represent around 80% of the market in some projections with Nvidia remains far ahead due to: CUDA software ecosystem, integrated networking and systems scale of production and customers.
• But custom chips (ASICs, TPUs) are growing fast due to multiple drivers: Lower cost per inference, better energy efficiency and large cloud providers designing their own silicon e.g. Broadcom and partners are shipping millions of custom AI processors and scaling revenue rapidly.
• Inference may overtake training: As discussed in a previous post, inference workloads could reach ~80% of compute demand within several years in some projections, which has several implications such as:
  • ASIC growth accelerates
  • Power efficiency becomes critical
  • Edge and on-premises deployments grow.
• Most analysts agree on this structure:
  • GPUs dominate training.
  • ASICs increasingly dominate inference for large scale workflows for hyperscalers, yet GPUs will continue to be deployed for small scale workflows that require flexibility and lower upfront costs (e.g. small and mid firms). 
  • Even if inference dominates compute hours, frontier training drives technology leadership labs want the fastest hardware available. That fact should keep Nvidia strategically important even if inference volume surpasses training, according to consensus.

• Nvidia: Could hold 70–75% of accelerator share by 2030 in some projections with clear leadership due to CUDA software ecosystem, strong developer adoption, full-stack systems (chips, networking, software, racks), and fast product cycles.
• AMD: Growing but still behind Nvidia in software ecosystem and deployment speed.
• Intel: Smaller share but still an important participant in accelerators.

In AI ASICs, the competitive landscape is different from GPUs because many of the most important chips are designed in-house by hyperscalers and never sold broadly: Google (TPU family), AWS (Trainium for training and Inferentia for inference) or Microsoft (Maia AI accelerator). That said, there are semiconductor developers focused on ASICs designing and manufacturing for third parties such as:

• Broadcom: Major designer of custom AI accelerators and networking silicon. Works with multiple hyperscalers on bespoke chips
• Marvell: Custom compute and interconnect chips.  Strong position in data-center infrastructure.
• Qualcomm: AI accelerators mainly for edge devices and PCs. Important in on-device inference rather than cloud training

Feel free to share your thoughts

• Some intelligence agencies such as Bloomberg projects the AI accelerator market could reach about $604 billion by 2033, growing at roughly 16% CAGR.
• Other analysts differ in methodology but all show rapid expansion such as Mordor Intelligence at ~$440 billion by 2030 or Fortune BI ~$309 billion by 2034. The exact number varies because definitions differ (GPU-only vs. all accelerators vs. full AI infrastructure), but the direction is consistent: multi-hundred-billion-dollar market this decade.
• Although GPU is leading with a 60% market share, ASICs are projected to grow at almost 30% CAGR within the 2026-2030 period.
• Drivers behind this significant growth:

• GPUs remain dominant: GPUs still account for the majority of accelerator revenue and are expected to represent around 80% of the market in some projections with Nvidia remains far ahead due to: CUDA software ecosystem, integrated networking and systems scale of production and customers.
• But custom chips (ASICs, TPUs) are growing fast due to multiple drivers: Lower cost per inference, better energy efficiency and large cloud providers designing their own silicon e.g. Broadcom and partners are shipping millions of custom AI processors and scaling revenue rapidly.
• Inference may overtake training: As discussed in a previous post, inference workloads could reach ~80% of compute demand within several years in some projections, which has several implications such as:
  • ASIC growth accelerates
  • Power efficiency becomes critical
  • Edge and on-premises deployments grow.
• Most analysts agree on this structure:
  • GPUs dominate training.
  • ASICs increasingly dominate inference for large scale workflows for hyperscalers, yet GPUs will continue to be deployed for small scale workflows that require flexibility and lower upfront costs (e.g. small and mid firms). 
  • Even if inference dominates compute hours, frontier training drives technology leadership labs want the fastest hardware available. That fact should keep Nvidia strategically important even if inference volume surpasses training, according to consensus.

• Nvidia: Could hold 70–75% of accelerator share by 2030 in some projections with clear leadership due to CUDA software ecosystem, strong developer adoption, full-stack systems (chips, networking, software, racks), and fast product cycles.
• AMD: Growing but still behind Nvidia in software ecosystem and deployment speed.
• Intel: Smaller share but still an important participant in accelerators.

In AI ASICs, the competitive landscape is different from GPUs because many of the most important chips are designed in-house by hyperscalers and never sold broadly: Google (TPU family), AWS (Trainium for training and Inferentia for inference) or Microsoft (Maia AI accelerator). That said, there are semiconductor developers focused on ASICs designing and manufacturing for third parties such as:

• Broadcom: Major designer of custom AI accelerators and networking silicon. Works with multiple hyperscalers on bespoke chips
• Marvell: Custom compute and interconnect chips.  Strong position in data-center infrastructure.
• Qualcomm: AI accelerators mainly for edge devices and PCs. Important in on-device inference rather than cloud training

Feel free to share your thoughts

• Some intelligence agencies such as Bloomberg projects the AI accelerator market could reach about $604 billion by 2033, growing at roughly 16% CAGR.
• Other analysts differ in methodology but all show rapid expansion such as Mordor Intelligence at ~$440 billion by 2030 or Fortune BI ~$309 billion by 2034. The exact number varies because definitions differ (GPU-only vs. all accelerators vs. full AI infrastructure), but the direction is consistent: multi-hundred-billion-dollar market this decade.
• Although GPU is leading with a 60% market share, ASICs are projected to grow at almost 30% CAGR within the 2026-2030 period.
• Drivers behind this significant growth:

• GPUs remain dominant: GPUs still account for the majority of accelerator revenue and are expected to represent around 80% of the market in some projections with Nvidia remains far ahead due to: CUDA software ecosystem, integrated networking and systems scale of production and customers.
• But custom chips (ASICs, TPUs) are growing fast due to multiple drivers: Lower cost per inference, better energy efficiency and large cloud providers designing their own silicon e.g. Broadcom and partners are shipping millions of custom AI processors and scaling revenue rapidly.
• Inference may overtake training: As discussed in a previous post, inference workloads could reach ~80% of compute demand within several years in some projections, which has several implications such as:
  • ASIC growth accelerates
  • Power efficiency becomes critical
  • Edge and on-premises deployments grow.
• Most analysts agree on this structure:
  • GPUs dominate training.
  • ASICs increasingly dominate inference for large scale workflows for hyperscalers, yet GPUs will continue to be deployed for small scale workflows that require flexibility and lower upfront costs (e.g. small and mid firms). 
  • Even if inference dominates compute hours, frontier training drives technology leadership labs want the fastest hardware available. That fact should keep Nvidia strategically important even if inference volume surpasses training, according to consensus.

• Nvidia: Could hold 70–75% of accelerator share by 2030 in some projections with clear leadership due to CUDA software ecosystem, strong developer adoption, full-stack systems (chips, networking, software, racks), and fast product cycles.
• AMD: Growing but still behind Nvidia in software ecosystem and deployment speed.
• Intel: Smaller share but still an important participant in accelerators.

In AI ASICs, the competitive landscape is different from GPUs because many of the most important chips are designed in-house by hyperscalers and never sold broadly: Google (TPU family), AWS (Trainium for training and Inferentia for inference) or Microsoft (Maia AI accelerator). That said, there are semiconductor developers focused on ASICs designing and manufacturing for third parties such as:

• Broadcom: Major designer of custom AI accelerators and networking silicon. Works with multiple hyperscalers on bespoke chips
• Marvell: Custom compute and interconnect chips.  Strong position in data-center infrastructure.
• Qualcomm: AI accelerators mainly for edge devices and PCs. Important in on-device inference rather than cloud training

Feel free to share your thoughts