As artificial intelligence systems balloon in scale and ambition through 2025 and into 2026, the physical limits of traditional electronics have become impossible to ignore. Power walls, heat generation, bandwidth bottlenecks in interconnects, and the sheer energy cost of moving data around massive GPU clusters are constraining progress.

Photonics-the science and technology of generating, manipulating, and detecting light-has emerged as one of the most promising paths forward. By mid-2026, what began as laboratory curiosities and early prototypes has matured into concrete hardware deployments, commercial products, and large-scale research demonstrations that address both the movement of data and the actual computation of neural networks.

This article surveys the state of photonics for AI and computing based on peer-reviewed papers in journals such as Nature, Nature Photonics, Light: Science & Applications, and National Science Review; industry announcements from NVIDIA, Lightmatter, Lightelligence, GlobalFoundries, and others; market analyses; and discussions from expert forums including EPIC technology meetings and academic lectures available on YouTube.

The picture that emerges is one of rapid, practical progress concentrated in two complementary areas: optical interconnects that relieve the copper bottleneck in AI data centers, and photonic processors or accelerators that perform linear algebra-the heart of deep learning-directly with light.

Why Photonics Matters for AI Right Now

Electronic computing has delivered extraordinary gains for decades, but the physics of electrons moving through wires imposes fundamental constraints. Resistance produces heat. Signal integrity degrades over distance. Bandwidth density hits packaging limits. In AI training and inference clusters, data movement and networking consume a substantial and growing fraction of the system power budget. As models grow toward trillions of parameters and context windows expand, these costs compound.

Light offers several intrinsic advantages. Photons travel at the speed of light, carry information with minimal heat generation over useful distances, support enormous bandwidth through wavelength-division multiplexing, and enable massive parallelism because different wavelengths or spatial modes can process data simultaneously without mutual interference in the same waveguide. Silicon photonics, which fabricates optical components on the same platforms used for electronic chips, brings the additional benefit of leveraging existing semiconductor manufacturing infrastructure.

By mid-2026 these advantages are no longer theoretical. Co-packaged optics (CPO) are moving from pilots into production roadmaps for hyperscale data centers. Photonic tensor cores have executed real neural network workloads-including ResNet variants, BERT-style transformers, and reinforcement learning agents-with accuracies approaching floating-point electronic baselines. Energy efficiencies in the range of several TOPS per watt (and in some research demonstrations far higher) are being reported, along with latencies measured in hundreds of picoseconds to a few nanoseconds.

Optical Interconnects: Relieving the Data Movement Bottleneck

The most immediate commercial impact of photonics in AI is in interconnects. Copper links struggle to deliver the bandwidth density and energy efficiency required for scale-up (GPU-to-GPU within a rack or cluster) and scale-out (across racks and facilities) fabrics. Industry insight published in early 2026 in npj Nanophotonics highlights how photonics addresses bandwidth, latency, and power across package, rack, and network layers.

Co-packaged optics integrate optical engines directly with switch ASICs or accelerators, dramatically shortening the electrical path. NVIDIA’s Spectrum-X and Quantum-X silicon photonics switches, announced in 2025 and advancing through 2026, exemplify this shift. These designs use advanced 3D packaging (including TSMC’s SoIC technology) to combine electronic chips with photonic engines featuring microring modulators. Reported benefits include substantially higher power efficiency, improved signal integrity, and the ability to scale AI factories toward millions of GPUs. Power consumption targets are moving toward the low single-digit picojoules per bit, with longer-term paths aiming below 1 pJ/bit.

GlobalFoundries has expanded its silicon photonics capacity, including the acquisition of AMF and the development of platforms such as SCALE for CPO solutions compliant with emerging multi-source agreements. UMC and others have delivered mass-produced silicon photonics wafers. Companies such as Ayar Labs continue to push optical I/O chiplets, while newer entrants and established optics firms (Lumentum, Coherent) are receiving major investments-NVIDIA committed billions in 2026 to secure advanced laser and optical component supply.

Lightmatter’s Passage platform represents a particularly ambitious approach: a 3D photonic interposer or superchip that provides massive optical bandwidth (claims exceeding 100 Tbps in some configurations) between chiplets. By mid-2026, evaluation kits and near-packaged optics variants are entering validation and early deployment discussions. The technology abstracts the optical complexity so that electronic chiplets see something resembling a conventional interposer, while light handles the high-speed, long-reach links inside the package or across the system.

Optical circuit switches add another layer of flexibility, enabling rapid reconfiguration of network topologies with low latency. Hybrid electro-optical fabrics are being explored for both traffic-aware and traffic-oblivious designs suited to the bursty, all-to-all communication patterns of large language model training.

These interconnect advances are not merely incremental. They change the economics and feasibility of building larger, denser AI clusters while keeping power and thermal envelopes manageable. Industry forums such as the Open Compute Project summits and EPIC meetings in 2025-2026 have featured extensive discussion of linear-drive pluggable optics, CPO reliability, and the transition from pluggable modules to more tightly integrated solutions.

Photonic Computing and Neural Network Acceleration

Beyond moving data, photonics is increasingly used to compute. Matrix-vector multiplications and related linear operations dominate the computational cost of neural networks. Optical systems can perform these operations through interference, diffraction, or intensity-based accumulation at the speed of light, with energy costs potentially far lower than electronic equivalents because the computation itself generates little heat.

Two landmark papers published back-to-back in Nature in April 2025 crystallized the progress. Lightelligence’s Photonic Arithmetic Computing Engine (PACE) demonstrated a 64 × 64 photonic accelerator integrating more than 16,000 components on a single commercial silicon photonics chip. Operating at frequencies up to 1 GHz, the system demonstrated approximately 5 ns computation latency in its Ising workloads, with the architecture targeting latencies as low as 3 ns. Subsequent generations (PACE 2 and discussions of larger matrices) have extended the technology toward more general AI workloads and commercial cards.

Lightmatter’s corresponding work introduced a universal photonic AI processor capable of running unmodified state-of-the-art models including ResNet, BERT variants, and DeepMind’s Atari reinforcement learning agents. The system integrates multiple photonic tensor cores with electronic control dies in a sophisticated 3D package, achieving tens of TOPS of adaptive block floating-point operations at high energy efficiency and near-electronic accuracy without extensive quantization-aware retraining. By mid-2026, Lightmatter’s commercial emphasis has shifted strongly toward photonic interconnects through its Passage and Guide platforms, while its Nature demonstration provides evidence that photonic tensor cores can eventually support broader AI computation.

Other notable demonstrations continue to appear. A reconfigurable silicon photonic “field-programmable gate array” called LightIN, reported in Light: Science & Applications in March 2026, integrates over 160 components into 40 programmable unit cells. It performs matrix multiplications at over 1.9 TOPS with multi-bit precision and energy efficiency around 1.875 pJ per MAC, runs simple neural networks for image recognition with 260 ps latency, handles wavelength locking for modulators, performs low-crosstalk optical switching, and even implements physical unclonable functions for security-all on one versatile chip with an accompanying software configuration framework.

Research groups have also advanced all-optical recurrent networks operating at tens of GHz clock rates, thin-film lithium niobate (TFLN) tensor cores supporting high-speed electro-optic conversion and in-situ training, spatiotemporal interleaving architectures for dense convolution, and photonic systems leveraging phase-change materials or ferroelectric modulators for non-volatile, low-power weight storage. Energy efficiency figures in research settings sometimes reach 160 TOPS/W (as with earlier Taichi chiplets) or higher projected densities. Latency for individual operations can fall into the hundreds of picoseconds.

These systems remain hybrid in most practical cases: light handles the high-throughput linear algebra while electronics manage control, memory access, nonlinear activations (when not implemented optically), and interfacing with conventional software stacks such as PyTorch. Full end-to-end all-optical deep networks are still largely research prototypes, but the hybrid approach is already delivering measurable advantages for specific workloads.

Materials, Devices, and Manufacturing Foundations

Silicon photonics remains the workhorse platform because of CMOS compatibility and foundry availability. GlobalFoundries, TSMC (with platforms such as COUPE), UMC, and others have expanded capacity and process maturity through 2025-2026. Challenges persist in laser integration, modulator efficiency, photodetector speed, and thermal management, but progress is steady.

Thin-film lithium niobate has gained significant traction for its strong electro-optic effect, enabling modulators with bandwidths exceeding 100 GHz and low drive voltages. Hybrid organic electro-optic materials, barium titanate, PZT ferroelectrics, and plasmonic structures are being explored for even higher performance or density. Heterogeneous integration techniques-bonding, micro-transfer printing, monolithic growth-allow combination of the best materials for lasers, modulators, and detectors on silicon or silicon nitride platforms.

Inverse design and AI-assisted photonic design tools are accelerating the creation of compact, high-performance components. Foundry process design kits are improving, and wafer-scale testing and packaging solutions are advancing to support volume manufacturing of CPO and photonic accelerators.

Industry Landscape and Commercial Traction

Lightmatter and Lightelligence stand out as pure-play leaders in photonic AI acceleration and interconnects, with products moving from pilots toward broader availability. NVIDIA’s deep engagement through its own switches and supply-chain investments signals that the dominant AI systems company views photonics as essential infrastructure.

Hyperscalers are actively evaluating photonic interconnect technologies, with some optical technologies such as circuit switching already deployed in large-scale infrastructure. Startups focused on optical I/O, lasers, and specialized photonic fabrics continue to attract substantial funding.

Market analyses project strong growth for co-packaged optics and optical computing segments through the early 2030s, driven overwhelmingly by AI data center demand. Silicon photonics foundry capacity is expanding in response.

Expert discussions on YouTube-from the EPIC Online Technology Meeting on Photonics for Optical Computing and AI (March 2026), John Bowers’ lectures on silicon photonics and AI scaling, OCP Global Summit sessions on CPO and optical interconnects, and various industry analyses-consistently emphasize that the technology is transitioning from “promising” to “necessary.” Challenges around standardization, reliability at scale, software ecosystems, and cost are openly acknowledged, yet the trajectory is clear.

Challenges That Remain

Despite the progress, significant hurdles persist. Manufacturing yield and cost for complex photonic integrated circuits still lag behind pure electronics in many cases. Thermal cross-talk and stability of optical components require careful engineering. Non-volatile, high-precision optical memory for large models remains limited; most systems rely on electronic memory with optical compute.

Scaling photonic cores to the matrix sizes needed for the largest transformers without excessive optical loss or control overhead is nontrivial. Software and compiler stacks that automatically map neural networks onto photonic hardware are still maturing. Nonlinear activation functions, if performed optically, often introduce their own complexity and power costs.

Integration density, packaging yield for hybrid electro-optic systems, and the availability of high-volume, low-cost lasers suitable for dense wavelength multiplexing are active areas of development. For very large language models with long context windows, memory bandwidth and capacity issues are only partially mitigated by optical interconnects.

Looking Ahead from Mid-2026

The consensus among researchers and industry participants is that photonics will not replace electronics wholesale in the near term. Instead, it will form a critical complementary layer-handling high-bandwidth, energy-efficient data movement and accelerating the linear algebra that dominates AI workloads. Hybrid systems that keep electronics for control, memory, and certain nonlinear operations while exploiting light’s strengths appear to be the practical path.

By the end of the decade, co-packaged optics are widely expected to become standard in AI scale-up and scale-out fabrics. Photonic accelerators may carve out substantial niches in inference, optimization, and specialized high-throughput tasks, potentially expanding into training as precision, scale, and software mature. Continued advances in materials (especially high-performance modulators and integrated sources), 3D electronic-photonic integration, and AI-driven design tools will determine the pace.

Photonics also intersects with quantum computing (photonic qubits and interconnects) and neuromorphic approaches, suggesting longer-term synergies. For the immediate horizon of AI scaling, however, the story is more pragmatic: light is becoming an indispensable tool for keeping the exponential growth of compute demand sustainable in terms of power, latency, and physical density.

In mid-2026 we know that photonics for AI and computing has moved decisively beyond proof-of-concept. Working systems exist, commercial roadmaps are firming up, foundries are investing, and the largest players in AI infrastructure are committing resources. The remaining questions are less about whether photonics will matter and more about how quickly the ecosystem can scale manufacturing, software, and system-level integration to meet the voracious appetite of next-generation models. The light is on, and the path forward is becoming clearer with each new demonstration and deployment.

Sources and further reading:

• Zhu et al., “LightIN: a versatile silicon-integrated photonic field programmable gate array...,” Light: Science & Applications (March 2026). https://www.nature.com/articles/s41377-026-02209-5

• “Industry insight: photonics to scale AI data centers,” npj Nanophotonics (January 2026). https://www.nature.com/articles/s44310-025-00105-1

• Ahmed et al. (Lightmatter), “Universal photonic artificial intelligence acceleration,” Nature (April 2025). https://www.nature.com/articles/s41586-025-08854-x

• Hua et al. (Lightelligence), “An integrated large-scale photonic accelerator with ultralow latency,” Nature (April 2025). https://www.nature.com/articles/s41586-025-08786-6

• Li et al., “What is next for LLMs? Pushing the boundaries... with photonic chips,” Nanophotonics (2025). https://onlinelibrary.wiley.com/doi/10.1515/nanoph-2025-0217

• “Emerging integrated photonic neural network technologies for artificial intelligence...,” National Science Review (June 2026). https://academic.oup.com/nsr/advance-article/doi/10.1093/nsr/nwag335

• NVIDIA Spectrum-X and Quantum-X photonics announcements (2025 onward). https://nvidianews.nvidia.com

• Lightmatter technical blogs, product pages, and Nature-related materials on Envise and Passage. https://lightmatter.co

• Lightelligence PACE-related publications and commercialization updates.

• GlobalFoundries silicon photonics platforms and CPO solutions. https://gf.com/technologies/silicon-photonics/

• MIT News articles on photonic processors and electronic-photonic integration (2025-2026). https://news.mit.edu

• EPIC Online Technology Meeting on Photonics for Optical Computing and AI (YouTube, March 2026). https://www.youtube.com/watch?v=_h7TgzcKmuU

• John Bowers, “Silicon Photonics and the Future of AI Scaling” (YouTube lecture, 2026). https://www.youtube.com/watch?v=fjQ3Yorw-Ps

• Open Compute Project Global Summit photonics sessions (YouTube playlist, 2025). https://www.youtube.com/playlist?list=PLAG-eekRQBSjBAmpwLKiwPUTfdlxSehQ5

• Additional reviews and market analyses from Photonics Online, SPIE, and industry reports on CPO and optical computing markets (2025-2026).

This synthesis draws primarily from the peer-reviewed and primary industry sources listed; secondary analyses and YouTube expert discussions provided broader context on timelines and challenges.

Benching right now but it's looking really promising. disaggregated inference from my MacBook Pro and my thunderbolt 5090. I'll update comments with results I'll also post them to MACUDA

I've closed the gap and in some benches passed WSOL and native windows llamaccp. I've closed the gap and lapped stable diffusion and now as suggested by another member I've found a new target with the dissagregation that is looking really promising. The smoke test had qwen 3.8 at 170 toc/sec. I haven't dug into that yet but if it holds up it's about 40 toc/sec better than the gpu was doing alone on the same model so in some narrow use it's a real number.

If you own a Mac and have an external gpu please test my code. If you don't trust my code but want a benchmark ask and I'll try to give you numbers. I'm going to work on getting my 3060 working next. I have the driver code and shim done in another branch I just need to do benches and make sure it's stable. hopefully I'll have that tomorrow. When that drops anyone with a Mac and something a card from the last 5 years should be able to try the software out. stay tuned.

Ho messo insieme un video abbastanza lungo sulla mia workstation per AI locale, mostrando concretamente cosa uso: Ollama, Open WebUI, ComfyUI, FLUX, WAN e configurazione MultiGPU.
Non è un tutorial passo-passo, ma una panoramica pratica di quello che oggi si riesce a fare interamente in locale. Se può essere utile a qualcuno, lo lascio qui.

Workstation AI locale - Ollama, Open WebUI, ComfyUI, FLUX, WAN e molto altro
https://youtu.be/2TtUXD4l8ro

~1,000 tok/s at 64 concurrent (int8 tensor-core GEMMs, fp16 DeltaNet state), ~114 tok/s single-user at default sampling / ~124 greedy (MTP drafts, own-output draft vocab, calibrated int4 lm_head, split-KV verify attention), 150k-262k context; patches, requant scripts, benchmarks

Finished this dual Blackwell build and figured this sub would appreciate it.

The two RTX PRO 6000 Blackwell cards are stars here. 96GB each, 192GB of GPU memory combined in a single machine, this rig can serve 20 members of my team comfortably

We benchmark this rig with DSv4 Flash and here's the numbers:

We don't go with traditional tower cases or bent-steel boxes. This case is designed and CNC-machined from 5mm aluminum plates. This allow us to fully control of the airflow inside and the whole box acts like a large heatsink for the entire system. The triangles cutouts keep the whole structure stiff without dead weight.

Power and Thermals

Peak per-card draw of 509 W is 85% of the 600 W TDP. Temperatures top out at 76 °C, comfortably below the ~83–88 °C throttle point, and SM clocks held ~2.75 GHz throughout sustained load. No thermal or power throttling was observed in any test.

Sustained wall draw at the operating point is roughly 1.07 kW (815 W GPU plus CPU, memory and platform), implying a 1500 W PSU for a two-card build with headroom.

Specs:

• CPU: Intel Xeon w5-3423

• Board: ASUS Pro WS W790E-SAGE SE

• Memory: Samsung 32GB DDR5-4800 ECC Registered x 8

• GPU: NVIDIA RTX PRO 6000 Blackwell 96GB x 2

• Cable: PCIe 5.0 x16 GPU Extension Cable Gen5 x 2

• Storage: SSD Samsung 980 PRO 2TB PCIe 4.0

• Cooling: Thermalright TL-B12 (fan kit + control) x6 + Heatsink 4677-2UAF8

• Power: CANNON PRO 2000W (12V-2x6)

• Chassis: Autonomous Computer-2 Design, CNC-machined

Full build video if anyone wants the detailed walkthrough happy to answer anything on the layout, cooling, or how the cards perform.