🗓 Mark your calendar! 👉 Wednesday and Thursday this week are arguably the two biggest market-moving days of the quarter. Microsoft and Alphabet report Wednesday, Amazon Thursday — and between them they’re now guiding to over half a trillion dollars in AI spending for 2026 alone, all revised upward from earlier estimates. ✅ If that spending is paying off in revenue, expect a rally. ❌ If it isn’t, expect the opposite — and the swing could be sharp either way (the options market is pricing moves as large as 7% in a single stock). Whether you hold these names directly or just own a broad US tech ETF, this is the week to be watching, not guessing. Follow Longbridge Singapore for daily market moves that matter to you. #LongbridgeSG# #LongbridgeAI# #AIchips# #microsoft# #amazon# #alphabet# #investing# #bigtech-------# AI-generated content may be inaccurate, incomplete, or based on outdated information, and should be independently verified before you rely on it. Pictures are included for illustration purposes and information is provided for reference only and shall not be construed as investment advice, recommendation, offer, invitation, solicitation or inducement. Investing involves risks and the price of investment products can rise or fall, sometimes significantly, which may lead to a loss of investment principal. Before making any investment decisions, it is highly recommended that investors carefully review the relevant sales documents and risk disclosure statements, seek independent professional advice and take into consideration all relevant risk factors based on their own needs and judgment. This advertisement has not been reviewed by the Monetary Authority of Singapore (MAS).

⚙ How GLM-5.3-Flash Served 70 Trillion Free Tokens on Chinese Chips GLM-5.3-Flash's anonymous "Ox Alpha" trial burned through roughly 70 trillion tokens in a week — and Zhipu says all of it ran on Chinese AI chip clusters. For many observers, that is a bigger story than the model itself. Zhihu contributor 恋猫 breaks down the systems engineering that made it work. His framing: the model architecture first reduces how much data needs to move, then the inference system compresses what remains. The reported scale is around 100,000 Chinese chips — from Huawei, Moore Threads and Hygon, according to @latepostnews — though @Zai_org itself has only said "tens of thousands." 1️⃣ The architecture cuts data movement first Versus GLM-5.3, Flash lowers attention compute by 3.01x and KV cache by 4.44x. Less data shuttling between memory and compute is the foundation everything else builds on. 2️⃣ Compute-for-bandwidth: trading FLOPs for HBM relief The bottleneck on these chips is HBM: the compute units still have headroom while memory bandwidth is nearly saturated. The fix, loosely speaking, turns "every step: read the full state, write the full state" into "read the state, recompute a little, write back periodically." A bit of extra matrix math buys a large reduction in HBM write traffic. 3️⃣ Communication-for-bandwidth: shard the cache across cards The cluster also uses high-speed inter-chip links and aggregated bandwidth to cut how much data each card must keep resident. The author's example: rank 0 holds the KV and indexer cache for some layers, rank 1 holds the rest. No card stores every layer's cache long-term; data is prefetched over the interconnect as each layer executes. The trade-off is real — more communication, far less resident cache per card. 4️⃣ EPD separation: three pools, scaled independently Finally, Encode, Prefill and Decode are split into three independently scalable resource pools, which matters most for multimodal traffic. Together, these pieces are what let the cluster run at high utilization. 5️⃣ The result: 3x end-to-end, cost "comparable to NVIDIA" Zhipu's own claim: end-to-end serving performance improved 3x on the same hardware, bringing per-token cost close to mainstream NVIDIA GPUs. The author's reading: individual Chinese cards may still be weaker, but model-system co-design lets the cluster as a whole reach international-mainstream throughput and cost. He adds a wry footnote: Zhipu's API used to be notorious among Chinese developers for 429 rate-limit errors. That it could absorb this launch's traffic at all — entirely on Chinese silicon — is, in his words, proof that optimization never ends. 🔗 Full Reading: #GLM# #Zhipu# #AIChips# #AIInfra# #LLM# #Inference# #OpenWeights#

💥 GLM-5.3-Flash: Reported Opus 4.8-Level Scores at One-Tenth the Price Zhipu has confirmed that Ox Alpha — the anonymous model that dominated OpenRouter and OpenCode last week — is GLM-5.3-Flash, a 320B-parameter MoE with 18B active, released with open weights and an API at roughly one-tenth of GLM-5.3's price. Zhihu contributor 小小将 opens with a confession: he had hoped Ox Alpha was an external team fine-tuning the open GLM weights. He was wrong — it was Zhipu's own model all along. His broader take: at this performance and this price, Flash becomes the new "kill line" for large models — the bar below which rivals simply get priced out. 1️⃣ The numbers behind the "kill line" Flash is somewhat larger than DeepSeek-V4 Flash, at 320B total and 18B active. On Zhipu's own benchmark comparisons, overall capability is roughly level with Claude Opus 4.8. More reference points he cites: 🔹 57 on the Artificial Analysis Intelligence Index — around GPT-5.6 Terra's level, slightly below GLM-5.3. 🔹 No.5 on Code Arena, one spot above GLM-5.3. 🔹 API at 1/10 of GLM-5.3's price, with a limited-time half-off promo bringing it to 1/20. 2️⃣ Same DeepSWE score, a fraction of the cost The author's sharpest comparison is on DeepSWE, where Flash scores 63% at a single-task cost of $0.24. DeepSeek-V4-Pro hits the same score at $1.67 per task — roughly seven times the cost for equivalent results. This is the author's cost arithmetic on reported figures, not an independent measurement. 3️⃣ The architecture that cuts the bill Part of the price drop is structural. Flash uses a hybrid of linear and sparse attention, sharply cutting attention compute, and a new IndexPool that compresses the indexer's cache from four copies to one — reducing latency and memory at million-token context. Versus GLM-5.3, the company reports attention compute down to about 1/3 and KV cache down to about 1/4.4. 4️⃣ The compute mystery, answered by domestic chips One reason few believed Ox Alpha was Zhipu's: the company was not thought to have enough spare compute for a massive free public test. The answer, per Zhipu: Flash is served from domestic Chinese chip clusters. The team built a custom inference engine on SGLang and worked around limited VRAM and bandwidth with quantization, layered deployment, and trading compute for communication — lifting end-to-end serving performance about 3x on the same hardware, with per-token cost now close to mainstream NVIDIA GPUs. 5️⃣ Chinese silicon just passed its biggest stress test The author's conclusion: Chinese chip clusters have shown they can carry large-scale inference for a frontier-level model — including a free, record-breaking public trial. If that holds, he argues, the outlook for Chinese models just got a lot brighter. 🔗 Full Reading: #GLM# #Zhipu# #GLM5Flash# #AIChips# #LLM# #AIInfra# #OpenWeights#

🔌 GLM-5.3-Flash Served Its Viral Debut Entirely on Domestic Chinese Chips Zhipu's GLM-5.3-Flash — the 320B-A18B model revealed this week as the anonymous "Ox Alpha" — set usage records on OpenRouter and OpenCode during its undercover test. The company says all of that traffic was served by domestic Chinese chip clusters. Zhihu contributor 刘延 reconstructs how Zhipu lined up this infrastructure, and reads the official engineering details for hints about which chips are actually doing the work. The core judgment: the bigger story here is not the model itself, but that a frontier-level model handled global-scale, real-world inference on domestic silicon. 1️⃣ The timeline behind the launch The author pieces together a sequence from public reporting. 🔹 Zhipu was reported to have acquired an infrastructure company. 🔹 LatePost reported Zhipu had brought 50,000 domestic cards online; around the same time, its CodePlan subscription got cheaper with generous bonus quotas. 🔹 Ox Alpha went live anonymously and, in the author's words, blew up worldwide. 🔹 Zhipu then confirmed every request in that test ran on domestic chips. 🔹 The latest LatePost report puts the deployment at 100,000 domestic cards. Note the card counts come from media reports, not Zhipu itself. 2️⃣ The engineering: surviving 1M context on constrained hardware Zhipu's own statement is unusually specific about the constraints. The main bottleneck on these chips is memory capacity and bandwidth, and supporting a 1M-token context is the hardest part. The company's listed optimizations include trading compute for bandwidth and communication for memory, intra-node tensor parallelism for the linear attention and the LM head, ReplaySSM, W8A8 quantization, INT8/FP8/BF16 mixed cache quantization, and Layer Split. 3️⃣ Which chips? Reading the precision hints Here the author speculates, and it should be read as inference, not confirmation. 🔹 FP8 support suggests Moore Threads could be handling prefill, or possibly Hygon's DCU-3. 🔹 INT8 points toward Ascend 910B/C as the likely backbone. 🔹 No mention of FP4 suggests the newer Ascend 950 is probably not in the mix. 4️⃣ Why this matters If the reporting holds, this is the first time domestic Chinese chip clusters have carried a frontier model's global production traffic at this scale — including a free, record-breaking stress test from developers worldwide. The author treats it as a proof point: China's domestic chips are no longer just for training experiments or internal pilots, but can serve a top-tier model to the open internet. 🔗 Key links: Official announcement: Open weights (MIT): 🔗 Full Reading: #GLM# #Zhipu# #AIChips# #AIInfra# #Ascend# #Semiconductors# #OpenWeights#

AI DEMAND ISN’T THE PROBLEM I’ve been saying for a while that AI demand isn’t what concerns me. It’s how much money these companies need to raise before their investments start generating meaningful returns. Now we’re seeing reports that Blackstone $BX expects Anthropic $ANT to receive a BB-range junk credit rating following its IPO, potentially making it more expensive to finance its massive AI infrastructure expansion. For context, Anthropic is involved in a $63B financing deal to lease 1.5 GW of Google’s AI chips, with Broadcom $AVGO guaranteeing more than $42B in senior loans and Blackstone investing $9B in junior debt yielding over 10%. That’s a lot of money, and those borrowing costs matter. But here’s why I’m not changing my long-term outlook on AI. Look at what we just heard from the hyperscalers: Google $GOOGL: Cloud revenue grew 103% YoY to $30.7B. Amazon $AMZN: AWS grew 39.3% YoY, its fastest growth in nearly five years. Amazon expects AWS revenue growth to exceed 40% in both 2027 and 2028. Google expects CapEx to increase significantly in 2027 as demand for its infrastructure continues growing. These are not small numbers, and they’re coming from businesses already generating substantial operating profits. The distinction I’m making is between demand and financing. I think the market is becoming increasingly concerned about how companies like Anthropic, OpenAI, and the neocloud providers will finance their expansion without putting too much pressure on their balance sheets. And I think that’s a legitimate concern. However, when I see Google Cloud more than doubling revenue and Amazon projecting another two years of 40%+ AWS growth, it’s difficult for me to argue that the demand isn’t there. My focus remains on which companies can turn that demand into sustainable free cash flow without constantly relying on outside financing. I remain bullish on AI, but I don’t believe every company funding this expansion will be a winner.

AI hardware just got so expensive that Wall Street has to step in, with over $90 BILLION in new debt to buy chips. According to the Wall Street Journal, Broadcom is seeking more than $50 billion to fund its custom AI chips for OpenAI. SpaceX is lining up $40 billion to buy Nvidia chips, with Apollo expected to lead. Oracle is in talks with Apollo and Goldman Sachs to cover the gap between paying for chips and getting paid for cloud. I think this is a HUGE signal that demand for compute is still exploding. $AVGO $ORCL $NVDA

#AI# chips are driving up testing demand. Taiwan #OSAT# Sigurd says customers are locking in long-term deals, with some memory players even paying premiums to secure capacity. 💡More: 🔗

AI’s biggest bottleneck is moving data and that could still create huge opportunities for optical networking companies (Save this) The chart shows a 1.6T optical transceiver, a device that transfers data between AI servers, switches, GPUs, and fiber optic networks. 1.6T means it can theoretically move up to 1.6 terabits of data per second, or 1,600 gigabits and that is about twice the speed of an 800G connection. This technology is important because AI data centers contain thousands of GPUs that must constantly exchange information. As AI models become larger, slow connections can leave expensive processors waiting for data but faster optical links help reduce that bottleneck and allow AI clusters to operate more efficiently. This image shows two directions of travel. The TX path converts electrical data from a server or switch into light which travels through fiber. The RX path receives that light and converts it back into an electrical signal for another device. And inside the module are several key components. Optical DSPs process and correct the signal, laser drivers control the lasers, modulators place data onto the light, and photodiodes convert incoming light back into electricity. Amplifiers, timing chips, thermal sensors, power management devices, and high speed connectors help the system operate reliably. Optical fiber becomes more attractive at higher speeds because copper connections lose efficiency over longer distances. At 1.6T, copper may only be practical across very short distances while optical technology can move data farther with better bandwidth and lower signal loss. This creates an investment opportunity beyond the companies making AI chips. NVIDIA remains a major beneficiary because its AI systems require fast connections between GPUs. Broadcom and Marvell could benefit from their networking chips, custom silicon, and optical connectivity products. Coherent and Lumentum are important optical suppliers with exposure to lasers, photonics, and high speed transceivers while Applied Optoelectronics is a more direct transceiver play and has announced a volume order for 1.6T data center products. Arista Networks and Cisco could benefit by selling the switches and networking systems that connect AI servers. Chinese suppliers such as Innolight, Eoptolink and Accelink Technology could also benefit as China expands its AI data center infrastructure. If you enjoyed reading this, make sure to follow @MelvinInvests for more photonics, AI infrastructure and semiconductor insights and turn on post notifications so you don't miss a single update. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, check out the link below: