SSD controller designer FADU’s new File-Semantic SSD (FS-SSD) was announced at the 2026 OCP Global Summit. The new device promises to move the file system into the SSD itself. As detailed in a white paper, this enables GPUs, DPUs, and NICs to open and read files with “no host filesystem, no block layer, and no storage-server CPU in the path.” FADU told Tom’s Hardware Premium it expects partner samples by Q3 2027.

FADU is a Seoul, South Korea-based fabless SSD controller designer. Its FS-SSD technology is designed to handle and improve the control path, as the data path side moved to accelerators years ago. FADU’s measurements show that the file system, plus block layers, can consume about 30% of CPU cycles in a GPUDirect Storage (GDS) random-read workload. It notes that bypass options give up the files, and the company claims its solution promises the best of both worlds.

In FADU’s own preliminary Network File System (NFS) over NVMe test, on a drive built on its FC6161 Gen6 controller in a PCIe 5.0 host, remote file throughput held steady as the server downshifted from what FADU labels 16 cores to just one, maintaining about 460,000 ops/s.

The NFSv3 operations are carried inside standard NVMe commands, and the drive handles path lookup, access control, and file-to-flash mapping. FS-SSD’s performance is remarkable when compared to Linux NFS-over-RDMA, which achieved a third of the ops/s with 16 cores and only 40K ops/s with one core. The technology remains “under active development,” and FADU told Tom’s Hardware Premium it expects Gen6-host results by Q1 2027, with partner samples in Q3 2027.

FADU chooses to use NFSv3 because it’s “small (21 operations), OS- and filesystem-agnostic, and stateless” with low firmware overhead. FS-SSD supports PCIe today, with NVMe-oF over RDMA and TCP working without any new transport needed. Reusing an existing protocol is a smart move, in our view. Under the hood, the file system and flash translation layer are merged with a file layer derived from F2FS. Node and data blocks are aligned with the natural flash pages, sections at corresponding erase units, so that the F2FS node table becomes a “File Map” pointing the node IDs at physical flash addresses.

One big advantage is that the DRAM mapping metadata overhead is considerably smaller: it uses the same amount of DRAM as a standard SSD, ~0.1% of NAND capacity (about 1 GB per TB), which a standard SSD fills almost entirely with its mapping table. Here, most of that DRAM, ~95%, can act as a hot-path node cache.

One garbage collector also replaces two, avoiding what FADU calls the “double GC” of host F2FS, where file-system cleanup and the drive’s own garbage collection amplify each other. Write amplification is also 63% lower versus a host F2FS solution on a mixed sequential and random workload with FADU’s research prototype, the company said. FADU told Tom's Hardware Premium that the prototype runs synthetic fio workloads on NVMeVirt, an open-source software SSD emulator from Seoul National University, so it is not a hardware result.

Most importantly for this method, it ensures host-free access. The GPU builds NFS-over-NVMe commands in queues in its own memory, which could include HBM. Alternatively, a remote SmartNIC or DPU with NVMe-oF target offload can drop commands in the drive’s submission queue. The data goes from SSD to NIC via PCIe peer-to-peer DMA, coming out as RDMA writes. FADU’s explicitly named use cases for this include GPU file reads and checkpoints, DPU-served HTTP content delivery, and a JBoF (just a bunch of flash) file server.

FADU’s numbers for its internal test come from a client and server Intel Core i5-13600K, a CPU with 20 threads that clocks up to 5.1 GHz with 128GB of DDR5, and a Mellanox ConnectX-6 100 Gbps NIC. Synthetic testing is compared using fio with the baseline F2FS and Ext4 on block firmware versus the FS-SSD via a kernel client and an SPDK user-space client. The sequential test is 1 thread, queue depth (QD) 128, with 128KB blocks, while random is at 4 threads, the same QD128, but with 4KB blocks.

These block sizes are typical for client workloads in our experience, so they are a fair measure, with detailed preliminary results in the table below. Against SPDK on the raw drive, figures FADU provided to us, FS-SSD’s SPDK client keeps about 93% on 4K random read and 53% on 4K random write, via fio, with around a 7% and 47% cost for the in-drive file layer. Though the raw drive’s 13,240 MB/s random write is above the ~12 GB/s FADU gives as the NAND’s program bandwidth, those runs likely weren’t at steady state. The Filebench result is particularly interesting, as the SPDK random gains apply to a plain block device too.

This should be the workload most favorable for the technology, as the Filebench file server is metadata-heavy with relatively high host overhead: FADU says it uses 10,000 files in a multi-level tree, mean directory width 20, and a mean file size of 128KB with 50 client threads.

One important thing to recall is that although this test is on a Gen6 drive, the test results are on a PCIe 5.0 host. Therefore, every sequential read config tops out at around 14.1 GB/s, which is roughly the link limit. The testbed also uses a consumer-class desktop CPU, while the one-core NFS server is a showcase example. The kernel client ends up 11.5% behind Ext4 on random read and 5.3% behind on random write, again by our math.

One example of a tough question more generally is: how do concurrent GPU, DPU, and host writers coordinate? How does a kernel mount’s page cache stay coherent with GPU access? NFSv3 storage devices, by the pNFS Flexible File Layout spec (RFC 8435), are “always treated as loosely coupled,” which means “Mandatory byte-range locking cannot be supported.”

Credentials riding in the NVMe command may also face security issues, as loosely coupled devices get only “a level of security equivalent to AUTH_SYS,” NFS’s basic scheme, in which the server trusts whatever user and group IDs the client sends. FADU’s published material also does not describe features such as snapshots, encryption handling, quotas, ACLs, crash handling, and so forth, related to this. Statelessness is what makes it work, but it doesn’t solve all problems, so the trust factor moves elsewhere.

In reply to our questioning, FADU said that there is no cross-host locking, as NFS’s lock manager isn’t implemented. The drive applies each write whole, in order. If two writes hit the same spot, the second one wins; this is “last-writer-wins.” Coordination depends on the clients: kernel-mounted ones get the usual advisory locks among themselves, GPU and SPDK clients coordinate in the application, and at scale a pNFS metadata server handles the coordination. Caching is also handled like ordinary NFS, with kernel clients, or is not done at all, for GPU and SPDK clients. This is stock NFSv3 behavior.

The drive has to check user/group IDs against the file permissions itself. Because those IDs ride inside the command, a client could fake them, but FADU says it closes that issue with capability-based authentication. Each command carries a capability issued by an outside authentication server, plus a code computed with a key shared only between that server and the drive. This is not detailed in its published material. FADU adds that support for the Kerberos authentication protocol is planned. For networked drives, the standard NVMe-oF protections are used, with only pre-approved hosts admitted. A drive trusts a single host’s kernel like any NVMe drive.

However, as FADU described to us, ACLs and other features are missing in the current release, aside from encryption, which in this case is handled by the FC6161’s standard TCG Opal self-encrypting drive support. Power loss is also typically handled with capacitors flushing metadata, the write buffer, and a log of in-flight operations. On restart, the drive replays the unfinished operations, and every write is durable if the drive acknowledges it.

FADU’s longer sights are on producing a JBoF storage solution with no server in front: FS-SSDs, a PCIe switch, and a DPU or NIC for handling NVMe-oF in hardware. FADU’s model puts this at about 42% lower power, about 57% lower cost per 16-drive server, and higher rack density with significantly lower five-year total cost of ownership. The drives would slot in behind existing pNFS metadata servers and could scale. FADU’s controllers already ship with drives such as the SN861, which Western Digital launched and SanDisk now sells, but this would expand the options and solutions it can provide.

Next year should bring real numbers and samples, with the JBoF reference design also expected. There may be an NVM Express or OCP Storage proposal down the line for a file-semantic command set to further cement and mature the approach. FADU has a good idea, is building it on an existing framework, and has a testbed with results and a hardware path forward on its Gen6 controller, with a trust model in mind. A Gen6-host run is now needed to support the company’s claims.

A fanless compute module will head to the Moon for supercomputer benchmarking, AI testing, and additional experimentation by NASA and Deloitte, as detailed in a Hewlett Packard Enterprise (HPE) release. The company’s Spaceborne Computer-4 will run what HPE calls “the first high performance computing (HPC) benchmarks on the surface of the Moon,” riding on an Astrolab FLIP rover to the lunar south pole. The launch is slated for late 2026 under NASA’s Commercial Lunar Payload Services initiative for a Moon Base program mission, in conjunction with a Voyager Technologies Griffin-1 lander.

Named benchmarks include High Performance Conjugate Gradients (HPCG), a supercomputing metric that complements High Performance LINPACK (HPL). AI testing will involve large language model (LLM) retrieval-augmented generation, vision inference, LiDAR processing, and an AI services container for AI app deployment. The LLMs “will deliver insights, analyze visual data, and support autonomous monitoring,” while the LiDAR processing involves mapping, navigation, terrain analysis, and infrastructure development.

Scott Wiest, HPE’s vice president, fellow and global sales chief technologist, said, “The next chapter of lunar exploration will depend on the ability to turn data into insights at the point of discovery.” HPE says “no other company or organization” has conducted such benchmarks on the Moon, with a presumption that no other parties’ planned HPC missions will get there first. HPE also assembled a software suite that handles the link to Earth-side systems and the secure delivery of results.

HPE’s “lightweight, self-contained, and fanless” advanced compute module is “built for space environments,” bringing AI and HPC “to the lunar edge,” as Wiest put it, or the “far-edge,” as HPE calls it. The computer blade is about 10 inches long and about 4 inches wide, about the size of a typical graphics card, and is made from two component cards, as HPE senior director Norm Follett explained on the company’s Technology Now podcast last year.

It includes 1TB of Kioxia storage, a low-power CPU, and a small GPU. The chassis is designed to shed heat into a rover plate, and the overall design is made for the Moon rather than being an off-the-shelf solution like International Space Station (ISS) units. Currently, HPE’s description of the unit lacks hard details on performance targets, power budgets, or chip vendors, but the eventual benchmark results will yield some information.

The original Spaceborne Computer launched in August 2017 on SpaceX’s CRS-12 to the ISS. It also ran HPL with HPCG, surpassing one teraflop, according to HPE. It was rack-installed and water-cooled; the fanless Computer-4 is quite miniaturized in comparison to the 124-pound original Spaceborne Computer. Spaceborne Computer-2, also on the ISS, is made of HPE Edgeline and ProLiant systems with Kioxia SSDs.

NASA will use the opportunity to run its Federated Learning for Unbounded and Intelligent Decentralization (FLUID) AI neural network, trained on data from the Earth, the Moon, and the ISS, coordinating three distinct models. The agency will also test a tuned LLM for semantic processing for handling onboard documents. Deloitte, for its part, will test its Silent Shield, “a new system to detect cyber intrusions” with near-real-time predictive analytics. Testing ideally occurs onboard, as the link to Earth is slow with low levels of bandwidth, Follett explained.

The launch is planned for the fourth quarter of 2026, using a SpaceX Falcon Heavy launch vehicle, SpacePolicyOnline reports. It will take several days for the lander to reach the Moon and its surface. The FLIP rover is designed to operate for one lunar day, or about 14 Earth days, before attempting to survive the night via hibernation, Follett said, making for a relatively tight benchmarking window.

Three contract workers died, and one more is in intensive care after an accident at the construction site for TSMC’s 2nm Fab 22 in Kaohsiung, Taiwan, reports Taiwan’s Central News Agency. The Southern Taiwan Science Park (STSP) Bureau suspects the cause was a broken bolt on a steel connection plate that sent the workers and steel components tumbling. Steel hoisting on the project has been halted, and the STSP has sent labor inspectors to investigate. The accident is not the first of its kind but rather follows at least a half dozen others since September 2024 at TSMC construction sites in Taiwan, according to local reports. Each of those earlier accidents killed one worker.

Local police said that the men were welding a steel beam on an upper floor and fell with it, while the STSP said the workers were performing steel-structure hoisting and installation when a third-floor connection-plate bolt apparently broke.

10 emergency vehicles were sent to the site with 22 personnel, and all four men involved in the incident were taken to the hospital. On-site medical staff gave first aid, but three of the workers died despite resuscitation efforts, and the fourth was moved to intensive care. The workers were employed by a contractor working on the fourth phase of the complex’s construction, and TSMC said the area was within that contractor’s scope of work.

According to the report, TSMC stopped the related construction work on its own and is working with authorities and supervising the subcontractor’s follow-up.

The Fab 22 complex, located in Kaohsiung’s Nanzi District, is part of the science park and is being built to accelerate 2nm manufacturing. TSMC entered high-volume 2nm, or N2, manufacturing in the fourth quarter of 2025, including at Fab 22. The complex has five phases planned, with a total investment TSMC puts at more than NT$1.5 trillion, or about $46 billion. The accident happened on the construction side, not in a producing fab. Kaohsiung’s deputy mayor said construction on Phase 4 began in August 2025, with operations expected in the fourth quarter of 2027.

Business Today reports that TSMC fab construction has increased, from four fabs a year in 2017–2024 to nine a year in 2025 and 2026. The deaths come as the company plans for 13 new leading-edge and advanced-packaging fabs in Taiwan, with company-wide capex of $60 billion to $64 billion this year.

The faster pace has encouraged the addition of a contractor’s safety record as a weight in selection and a plan for a Taiwan Semiconductor Construction Engineering Safety Association with construction partners. TSMC Chairman C.C. Wei said at last year’s shareholders meeting that safety cannot be compromised, and the company’s 2025 annual report said it will keep investing in fabs across several locations in Taiwan “with a strong emphasis on construction site safety.”

Cliff Hou, TSMC senior vice president and deputy co-COO, told Business Today that increased worker count and overlapping construction have raised the risk, requiring careful supervision, and that TSMC’s contractor lost-time injury rate had fallen about 42% from last year’s high as of late May, when the publication toured the Nanzi site to see the overhaul. It’s too early to call the program a failure, with the cause as yet unknown.

The accident comes less than a week after an explosion killed two people at Intel’s chip plant in Israel, though that was an operating fab rather than a construction site. Both accidents come as chipmakers race to meet demand, with global chip sales passing $1 trillion through August, according to the Semiconductor Industry Association.

The STSP’s findings will determine what penalties, if any, will fall under Taiwan’s Occupational Safety and Health Act, while it assists the victims’ families. No date has been given at this time for that or for the Phase 4 steel hoisting restart. Nor has TSMC commented on any schedule impact.

The game of Minecraft is no match for a team of four Gemini 4 Argon agents, as they were able to defeat it in 27 hours with 2,783 self-written programs, a paper from Google DeepMind reveals. The agents did die 605 times, even on Easy, and about half the run was spent fighting the Ender Dragon, the final boss of the game. The run finally came to completion when the End was solved by stacking dirt to reach the boss’s crystals, at an estimated cost of around $520 in tokens.

The experimental run was designed from the get-go with one goal: kill the Ender Dragon. The playthrough involved a random, shared survival world on Easy with four agents — a fifth agent existed only for reporting — from Gemini 4 Argon, Google’s latest frontier model. Each agent had its own player through the open-source Mineflayer JavaScript library for Minecraft bots, but they were able to coordinate through in-game chat and shared notes. Every program used in the run was written by the agents themselves, and the emphasis is on coding and teamwork rather than game-playing.

The authors say this is the “first end-to-end autonomous completion of Minecraft by AI agents,” at least “to our knowledge,” qualified by the use of Mineflayer “without pre-scouted seeds or other game-specific tools written by humans.” Mineflayer tells the agents where blocks and creatures are, with a pathfinding plugin to plan routes. There was “no human input during the run” and “no one assigned roles or wrote game logic.” The run, starting with the first program and ending with the dragon's death, took 27 hours and 14 minutes.

Although the agents were not given roles, within about 10 minutes a division of labor was proposed in chat by bot janesick. Bot Auratian offered an alternative plan while Bot Gyldenia acknowledged it. The roles ended up being: Gyldenia for food, beds, and flint; Bulicame for diamonds; Janesick for iron and tools; Auratian for the Nether portal. Moments of losses, such as a Ghast fireball destroying dropped items, led the team to diagnose the cause and write rules to prevent it in the future.

Finding the stronghold involved janesick, who threw two Eyes of Ender from different spots and sampled each eye’s flight once every 50 ms, triangulating the location. Carrying the eyes involved its own dire moment when Auratian drowned during the task, but the spot was noted, and the eyes were rescued.

The eventual fight with the dragon led to 69 deaths, and the ten crystals kept regenerating the boss until Auratian discovered they could use dirt pillars to reach the crystals. Four hours and 40 minutes later, all ten crystals were downed, and the dragon was dead 39 minutes after that.

The agents operated by writing short JavaScript programs, almost 2,800 in total, with 42 helper functions kept alive between programs, while a Mineflayer bot ran them in the shared world. Text was returned with status data, not pictures. This is a shared approach to the non-screenshot GPT-6 Astra clear of the Orc starting zone in World of Warcraft earlier this month. AI agents playing games is an ongoing trend, with Pokémon Red probably being the most popular, a game defeated in multiple ways, including with a Jev decision model layer.

Minecraft has also been a popular target, and the authors admit this, highlighting two earlier community dragon kills: a planner with an action-selection model on a fixed speedrun seed in Peaceful mode, and a single Claude Opus 5 agent that used 37 ready-made, high-level tools on Normal, versus Mineflayer alone for the Gemini team. AI Bengineering’s Claude run, relying mostly on Opus 5 at high effort, took 3 hours and 56 minutes from start — “no human input after the prompt” — to kill with 388 tool calls and only one death. The cost in that case, at API-equivalent rates, was about $93.

Google’s Minecraft run, using Gemini 4 Argon’s 50% introductory pricing, which runs from initial API access that followed the model announcement through Nov. 30, cost more than half a grand; at normal pricing, that’s more than a grand. A GPT-6 Astra Portal run last month, which took about 24 hours, came in at $571 in tokens.

Google’s run took about 1.35 billion input tokens with 91% served from cache. About 24,000 model calls involved around 16 million output tokens, a quarter of them thinking. In total, if we’re also including the observer agent, the session would be estimated at $810, or $1,620 after the promotion, at list prices.

Each player is driven by a “general-purpose Gemini 4 Argon coding agent,” which is a fancy way of saying that it’s not specialized for Minecraft. Gemini 4 Argon itself is a frontier model for “real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense,” according to a company blog. With Minecraft, 22% of the agents’ programs ended in an error, with more than 600 deaths.

Although the model was used for a game here, the run was designed to show that Argon is capable of “complex, long-horizon workflows” for complex real-world tasks and agent coordination. Such long projects can involve decompiling a game back into source code, as we covered recently with a Modern Warfare 2 rebuild that now has 83% of its functions matching the original byte for byte.

A Russian military satellite, Cosmos 2612, has closed to roughly 3 miles, from a previous distance of more than 200 miles, of the U.S.-owned radar satellite that operates in a fleet that supports Ukraine, Axios reports. The unapproved rendezvous in low Earth orbit, which astronomer Jonathan McDowell says may be the first of its kind, raises alarm in the West. The position change, which was likely not an accident as fuel is scarce, is probably a provocation and an attempt to spy on the satellite.

LeoLabs said in a statement Friday that the movement “appears to be a deliberate approach” toward ICEYE-X36, with uncertain intent and behavior that “warrants continued monitoring.” Roughly 3 miles is too close for comfort in satellite terms: there is no strict legal limit on how close satellites can get, but countries generally keep at least 6 miles (9.7 km) apart, University of Mississippi space law professor Aaron Brynildson told Axios. The approach was made without the owner’s consent, and Russia hasn’t said what the military satellite carries.

Some clues may come from the target’s operational purpose. ICEYE-X36 is a synthetic aperture radar (SAR) satellite which can image through cloud cover and at night. It was launched in March 2024 on a SpaceX Falcon 9. It’s registered to ICEYE US, the U.S. arm of the Finland-founded company. The satellite, which is in a roughly 545 km (339 mi) sun-synchronous orbit, is part of a fleet that has supplied Ukraine with information since 2022. This includes a deal earlier this year with a customer in Ukraine’s defense ministry, but a Ukrainian official told Axios it’s unclear whether ICEYE-X36 itself supports the country; ICEYE and the Defense Department declined to comment.

In May, the majority of the six military satellites Russia launched in April via a Soyuz-2.1b, Cosmos 2609–2614, shifted their orbits about 0.8 degrees to match X36’s orbital plane. Cosmos 2609 passed within 2.5 km (1.6 mi) on May 26. Cosmos 2612, on the other hand, drifted off X36’s plane by about 0.24 degrees by Sept. 30, up to about 28 km side to side, according to Greg Gillinger, a space analyst with Integrity ISR.

Then, Cosmos 2612 made separate burns from Sept. 30 through Oct. 16 to match altitude, eccentricity, and orbital period, Dutch satellite tracker Marco Langbroek wrote. By Saturday night, he estimated from public orbital data, it was within “a few hundred meters or possibly (much) closer.”

These movements appear deliberate and cost a lot of fuel, which the Center for Strategic and International Studies calls “a limited resource on a satellite.” Close-proximity work can be benign, it says, but policymakers should assume the Russian satellite “could take an action that threatens the commercial satellite,” and the U.S. and Europe should decide how to respond to any hostile act in space and make a statement on the decision. Back in July 2020, Russia’s Cosmos 2543 released an object at up to 186 m/s (416 mph) relative to itself, an action which was condemned by the U.S. and U.K.

Intent was less clear earlier this year, when McDowell told Meduza the orbital data showed no sign of the stable, close distance that tracking ICEYE-X36 would require. Now, he says it is an unapproved meeting and is not alone. Gillinger says the setup “removes any remaining doubt for me that Cosmos 2612 is interested in ICEYE X36.” Langbroek adds that “I was skeptical” at first but now calls it “completely non-consensual,” though he cautions it would be difficult to pin any X36 failures on 2612’s actions.

In a Reuters report less than two weeks ago, ICEYE CEO Rafal Modrzewski said the company moved its satellite after May, only to draw more Russian maneuvers, which Germany’s Space Command head, Maj. Gen. Michael Traut, described as “chasing.” Belinda Marchand, chief scientist at U.S. space intelligence firm Slingshot Aerospace, added that the moves may have been meant for surveillance, to prepare for future interference, or to signal capability. Western officials reportedly describe such trailing as a way to gather intelligence and test the West’s tolerance.

No statement from Russia is on record. Gillinger also told readers to “watch Cosmos 2611 and 2613, whose planes are converging on ICEYE X37’s,” with crossings expected around Nov. 6 and Nov. 15.