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NVIDIA and Meta Announce Expanded Collaboration on Muse Models

On October 27, 2026, NVIDIA and Meta announced an expansion of their collaboration on Muse and the Muse series of models to improve inference and training performance, with Meta's Muse models and agents running on NVIDIA infrastructure. According to an announcement published by the NVIDIA Newsroom that day, Meta plans to expand deployment of NVIDIA Vera Rubin and Vera CPU and to adopt Spectrum-X Ethernet and BlueField-4; the two sides will also evaluate the NVIDIA OpenShell reference architecture and NVIDIA Sentry for secure agent deployment and behavior monitoring. The same announcement stated that NVIDIA will invest $3 billion in Meta's equity offering. 36Kr AI reported on October 28 that this investment is part of Meta's total $95 billion fundraising plan, with AMD and Broadcom also expressing interest in investing; Meta expects full-year capital expenditure to reach $135 billion to $145 billion. The collaboration and investment plans have now been announced, while subsequent deployment progress remains to be disclosed.

NVIDIA and Meta announce expanded Muse model collaboration

On October 27, 2026, NVIDIA and Meta announced an expansion of their collaboration on Muse and the Muse series of models to improve inference and training performance, with Meta's Muse models and agents running on NVIDIA infrastructure. According to an announcement published by the NVIDIA Newsroom that day, Meta plans to expand deployment of NVIDIA Vera Rubin and Vera CPU and to adopt Spectrum-X Ethernet and BlueField-4; the two sides will also evaluate the NVIDIA OpenShell reference architecture and NVIDIA Sentry for secure agent deployment and behavior monitoring. The same announcement stated that NVIDIA will invest $3 billion in Meta's equity offering. 36Kr AI reported on October 28 that this investment is part of Meta's total $95 billion fundraising plan, with AMD and Broadcom also expressing interest in investing; Meta expects full-year capital expenditure to reach $135 billion to $145 billion. The collaboration and investment plans have now been announced, while subsequent deployment progress remains to be disclosed.

View story:NVIDIA and Meta Announce Expanded Collaboration on Muse Models

NVIDIA and Oracle Announce Expansion of Vera Rubin to Multi-Gigawatt Scale

On October 28, 2026, NVIDIA and Oracle announced plans to expand the NVIDIA Vera Rubin NVL72 systems in OCI Superclusters to multi-gigawatt scale, while Vera Rubin NVL72 and the NVIDIA Vera CPU became generally available on OCI. According to information published by the NVIDIA Newsroom that day, OCI Superclusters connect 72 GPUs per rack via NVLink and use Spectrum-X Ethernet and BlueField-4 DPUs; the two companies said customers can scale CPU and GPU resources independently to reduce idle capacity. On the same day, Analytics India Magazine reported that at the Oracle AI World conference, Oracle announced an expansion of AI compute capacity on its cloud platform, adding NVIDIA Vera Rubin NVL72 systems and Vera CPUs, and plans to offer Google TPU and Cerebras inference technology on OCI starting in 2027. The NVIDIA information also noted that OpenAI has already trained and run GPT-6 Astra on more than 100,000 NVIDIA Blackwell GPUs on the OCI Supercluster in Abilene, and said it will put Vera into production to support agent sand

NVIDIA and Oracle announce expansion of Vera Rubin to multi-gigawatt scale

On October 28, 2026, NVIDIA and Oracle announced plans to expand the NVIDIA Vera Rubin NVL72 systems in OCI Superclusters to multi-gigawatt scale, while Vera Rubin NVL72 and the NVIDIA Vera CPU became generally available on OCI. According to information published by the NVIDIA Newsroom that day, OCI Superclusters connect 72 GPUs per rack via NVLink and use Spectrum-X Ethernet and BlueField-4 DPUs; the two companies said customers can scale CPU and GPU resources independently to reduce idle capacity. On the same day, Analytics India Magazine reported that at the Oracle AI World conference, Oracle announced an expansion of AI compute capacity on its cloud platform, adding NVIDIA Vera Rubin NVL72 systems and Vera CPUs, and plans to offer Google TPU and Cerebras inference technology on OCI starting in 2027. The NVIDIA information also noted that OpenAI has already trained and run GPT-6 Astra on more than 100,000 NVIDIA Blackwell GPUs on the OCI Supercluster in Abilene, and said it will put Vera into production to support agent sand

View story:NVIDIA and Oracle Announce Expansion of Vera Rubin to Multi-Gigawatt Scale

SWI Compute Emerges from Stealth and Unveils European NVIDIA AI Factory Plans

On October 26, 2026, SWI Compute emerged from stealth and announced that it will build and operate NVIDIA AI factories in Europe. According to Unite.AI, the plan is backed by 3.6GW of power capacity and approximately $1 billion in NVIDIA system orders. The first factory is located in Cambridge, UK, with an initial scale of 30MW, deploying nearly 7,000 Rubin GPUs and 3,500 Vera CPUs, and is expected to come online by the end of 2027, with plans to expand to 2.3GW by 2029. The company says that starting in 2027, new sites will adopt the NVIDIA DSX AI Factory platform and the Vera Rubin reference design, which can increase GPU capacity by up to 40% within the same power budget. There are currently no subsequent developments.

SWI Compute emerges from stealth and announces plans for European NVIDIA AI factories

On October 26, 2026, SWI Compute emerged from stealth and announced that it will build and operate NVIDIA AI factories in Europe. According to Unite.AI, the plan is backed by 3.6GW of power capacity and approximately $1 billion in NVIDIA system orders. The first factory is located in Cambridge, UK, with an initial scale of 30MW, deploying nearly 7,000 Rubin GPUs and 3,500 Vera CPUs, and is expected to come online by the end of 2027, with plans to expand to 2.3GW by 2029. The company says that starting in 2027, new sites will adopt the NVIDIA DSX AI Factory platform and the Vera Rubin reference design, which can increase GPU capacity by up to 40% within the same power budget. There are currently no subsequent developments.

View story:SWI Compute Emerges from Stealth and Unveils European NVIDIA AI Factory Plans

Nvidia deploys industrial AI systems at Siemens' Erlangen factory

On October 24, 2026, Nvidia deployed systems including digital twins, AI-generated training data and AI agents at Siemens' electronics factory in Erlangen, Germany, to accelerate production and reduce costs. The two companies said that using digital twins to generate realistic images of PCB assemblies to pretrain inspection systems can cut engineering effort by up to 85% and shorten time to market by up to 40%. The factory already uses more than 100 AI algorithms for production, logistics, quality inspection and maintenance, and Siemens plans to invest about $559 million in upgrading the plant by 2030. Analysts noted that these savings figures come from Siemens' own high-mix factory, and whether they can be replicated at existing multi-vendor sites remains to be verified. There are currently no subsequent developments.

gpu-unlock 1.0.0 provides FP/BF16 unlock for Nvidia 3000 series

The open-source project gpu-unlock 1.0.0 provides an FP/BF16 instruction issue rate unlock for Nvidia 3000 series graphics cards. According to a report dated October 24, 2026, this Linux kernel module can complete the unlock before the NVIDIA driver binds to the graphics card, doubling the FP/BF16 FMA instruction issue rate of GA102, GA103, GA104, and GA106 graphics cards to bring them to professional-card levels, thus eliminating the need to manually patch after every driver update. In measured tests, the RTX 3090 Ti's BF16 rose from 87.50 TFLOPS to 157.41 TFLOPS, FP16 from 87.46 to 118.39 TFLOPS, and TF32 from 42.58 to 82.61 TFLOPS. Subsequent reports stated that the tool lifts the restriction by rewriting the speed select register, with no need to flash the VBIOS or modify the driver; the RTX 3090 Ti's BF16 improves by about 80%, ComfyUI image generation throughput improves by 56% to 59%, and the RTX 3090's prompt prefill throughput under vLLM improves by about 33%, but the Decode stage of per-token generation sees almost no improvement due to VRAM bandwidth limits. This modification is not officially supported by Nvidia, and you must bear the power consumption, cooling, and warranty risks yourself.

The report says Amazon purchased Nvidia racks in advance but lacks power for deployment.

According to internal emails seen by Business Insider, Amazon purchased approximately $5 billion worth of Nvidia GB300 server racks in advance, but some U.S. AWS sites lack sufficient power, so the hardware can only be temporarily stored in transit warehouses. The report says AWS alone is expected to receive about $5 billion worth of GB300 racks in August, with roughly another $3 billion added each month thereafter. This phenomenon, known as dark GPUs, is putting financial pressure on data centers; Amazon's second-quarter free cash flow turned negative for the first time since 2023, and capital expenditures this year are expected to be about $220 billion. There are currently no subsequent developments.

Report: Amazon Procured Nvidia Racks Early but Lacks Power to Deploy Them

According to internal emails seen by Business Insider, Amazon purchased approximately $5 billion worth of Nvidia GB300 server racks in advance, but some U.S. AWS sites lack sufficient power, so the hardware can only be temporarily stored in transit warehouses. The report says AWS alone is expected to receive about $5 billion worth of GB300 racks in August, with roughly another $3 billion added each month thereafter. This phenomenon, known as dark GPUs, is putting financial pressure on data centers; Amazon's second-quarter free cash flow turned negative for the first time since 2023, and capital expenditures this year are expected to be about $220 billion. There are currently no subsequent developments.

View story:The report says Amazon purchased Nvidia racks in advance but lacks power for deployment.

Nvidia deploys industrial AI systems at Siemens Erlangen plant

On October 24, 2026, Nvidia deployed systems including digital twins, AI-generated training data and AI agents at Siemens' electronics factory in Erlangen, Germany, to accelerate production and reduce costs. The two companies said that using digital twins to generate realistic images of PCB assemblies to pretrain inspection systems can cut engineering effort by up to 85% and shorten time to market by up to 40%. The factory already uses more than 100 AI algorithms for production, logistics, quality inspection and maintenance, and Siemens plans to invest about $559 million in upgrading the plant by 2030. Analysts noted that these savings figures come from Siemens' own high-mix factory, and whether they can be replicated at existing multi-vendor sites remains to be verified. There are currently no subsequent developments.

View story:Nvidia deploys industrial AI systems at Siemens' Erlangen factory

gpu-unlock 1.0.0 provides FP/BF16 unlock for Nvidia 3000 series

The open-source project gpu-unlock 1.0.0 provides an FP/BF16 instruction issue rate unlock for Nvidia 3000 series graphics cards. According to a report dated October 24, 2026, this Linux kernel module can complete the unlock before the NVIDIA driver binds to the graphics card, doubling the FP/BF16 FMA instruction issue rate of GA102, GA103, GA104, and GA106 graphics cards to bring them to professional-card levels, thus eliminating the need to manually patch after every driver update. In measured tests, the RTX 3090 Ti's BF16 rose from 87.50 TFLOPS to 157.41 TFLOPS, FP16 from 87.46 to 118.39 TFLOPS, and TF32 from 42.58 to 82.61 TFLOPS. Subsequent reports stated that the tool lifts the restriction by rewriting the speed select register, with no need to flash the VBIOS or modify the driver; the RTX 3090 Ti's BF16 improves by about 80%, ComfyUI image generation throughput improves by 56% to 59%, and the RTX 3090's prompt prefill throughput under vLLM improves by about 33%, but the Decode stage of per-token generation sees almost no improvement due to VRAM bandwidth limits. This modification is not officially supported by Nvidia, and you must bear the power consumption, cooling, and warranty risks yourself.

View story:gpu-unlock 1.0.0 provides FP/BF16 unlock for Nvidia 3000 series

NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the model has 8B total parameters, 4B effective parameters, and a 64K context, and is post-trained using only reinforcement learning based on Google Gemma 4 E4B; the Q4_K_M version occupies 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. The report also states that in NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B in 15 of 21 benchmarks, but Tau2-Bench is 55.1, trailing Qwen3.5-4B's 75.1.

NVIDIA launches DSX POD modular AI factory reference design

On October 21, 2026, NVIDIA unveiled the DSX POD modular AI factory reference design at the GTC conference in Berlin, targeting Vera Rubin AI factories. The design is integrated and validated by partners inside modular containers before delivery, moving installation work such as compute, networking, cabling, and liquid cooling forward into the manufacturing stage. NVIDIA's press release states that the solution can shorten deployment time at a ready site from six months to as little as 14 days; a same-day report by Zhidongxi mentioned that Jensen Huang said adopting the solution can compress the time from compute arriving on site to going into production from 4 to 5 months to about 1 month. Partners include Dell, HPE, Lenovo, Foxconn (Ingrasys), QCT, and others, with global deliveries planned for early 2027. There are currently no subsequent developments.

NVIDIA launches DSX POD modular AI factory reference design

On October 21, 2026, NVIDIA unveiled the DSX POD modular AI factory reference design at the GTC conference in Berlin, targeting Vera Rubin AI factories. The design is integrated and validated by partners inside modular containers before delivery, moving installation work such as compute, networking, cabling, and liquid cooling forward into the manufacturing stage. NVIDIA's press release states that the solution can shorten deployment time at a ready site from six months to as little as 14 days; a same-day report by Zhidongxi mentioned that Jensen Huang said adopting the solution can compress the time from compute arriving on site to going into production from 4 to 5 months to about 1 month. Partners include Dell, HPE, Lenovo, Foxconn (Ingrasys), QCT, and others, with global deliveries planned for early 2027. There are currently no subsequent developments.

View story:NVIDIA launches DSX POD modular AI factory reference design

NVIDIA showcases full lineup of local AI hardware products at GTC Berlin

On October 21, 2026, NVIDIA showcased three local AI hardware lines—RTX Spark, DGX Spark, and DGX Station—at GTC Berlin, and announced multiple software updates. Official information shows that DGX Spark 64GB goes on sale on October 23; NVIDIA Sync will support clusters of up to eight DGX Sparks and will add Model Launcher this month; DGX Station for Windows is expected to launch in December; the Recursive Flint local coding model is released with open-source Apache 2.0 weights. A follow-up report on October 23 stated that DGX Spark officially supports eight-node clusters, with unified memory expandable to 1TB, and can locally run frontier models such as the full Z.ai GLM 5.3 (753B); official benchmarks show that Qwen3.8-27B (NVFP4) decoding throughput increases from about 12 tokens/s on a single node to 52 tokens/s on eight nodes, with eight nodes achieving a 4.43x decoding speedup, and nanochat pretraining achieving a 7.79x speedup with 97.4% scaling efficiency. NVIDIA Sync's eight-node cluster support will be available before the end of the year.

NVIDIA showcases its full line of local AI hardware products at GTC Berlin

On October 21, 2026, NVIDIA showcased three local AI hardware lines—RTX Spark, DGX Spark, and DGX Station—at GTC Berlin, and announced multiple software updates. Official information shows that DGX Spark 64GB goes on sale on October 23; NVIDIA Sync will support clusters of up to eight DGX Sparks and will add Model Launcher this month; DGX Station for Windows is expected to launch in December; the Recursive Flint local coding model is released with open-source Apache 2.0 weights. A follow-up report on October 23 stated that DGX Spark officially supports eight-node clusters, with unified memory expandable to 1TB, and can locally run frontier models such as the full Z.ai GLM 5.3 (753B); official benchmarks show that Qwen3.8-27B (NVFP4) decoding throughput increases from about 12 tokens/s on a single node to 52 tokens/s on eight nodes, with eight nodes achieving a 4.43x decoding speedup, and nanochat pretraining achieving a 7.79x speedup with 97.4% scaling efficiency. NVIDIA Sync's eight-node cluster support will be available before the end of the year.

View story:NVIDIA showcases full lineup of local AI hardware products at GTC Berlin

NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the model has 8B total parameters, 4B effective parameters, and a 64K context, and is post-trained using only reinforcement learning based on Google Gemma 4 E4B; the Q4_K_M version occupies 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. The report also states that in NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B in 15 of 21 benchmarks, but Tau2-Bench is 55.1, trailing Qwen3.5-4B's 75.1.

View story:NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

MiniMax thanks NVIDIA NeMo for day-0 fine-tuning support for M3.1-Flash

On October 23, 2026, MiniMax publicly thanked the NVIDIA NeMo team, expressing gratitude for providing day-0 fine-tuning support for MiniMax-M3.1-Flash. According to information officially released by MiniMax, the cited post by Huiying Li states that the model had already processed 200T+ tokens through Space Bunny before its public release, and that the weights are now open. The official statement also mentions that NeMo-AutoModel's day-0 fine-tuning support covers FSDP2, expert parallelism, pipeline parallelism, and context parallelism, can scale to 128 GPUs, and can directly load NVFP4 checkpoints. Currently, this event has only this one official fact, with no subsequent developments.

MiniMax thanks NVIDIA NeMo for day-0 fine-tuning support for M3.1-Flash

On October 23, 2026, MiniMax publicly thanked the NVIDIA NeMo team, expressing gratitude for providing day-0 fine-tuning support for MiniMax-M3.1-Flash. According to information officially released by MiniMax, the cited post by Huiying Li states that the model had already processed 200T+ tokens through Space Bunny before its public release, and that the weights are now open. The official statement also mentions that NeMo-AutoModel's day-0 fine-tuning support covers FSDP2, expert parallelism, pipeline parallelism, and context parallelism, can scale to 128 GPUs, and can directly load NVFP4 checkpoints. Currently, this event has only this one official fact, with no subsequent developments.

View story:MiniMax thanks NVIDIA NeMo for day-0 fine-tuning support for M3.1-Flash