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Tag: Broadcom

  • OpenAI Jalapeño Chip Beats Blackwell 1.9x Per Watt — Ships 2027

    The OpenAI Jalapeño chip, the company’s first custom inference ASIC, delivered 1.5x to 1.9x more AI work per watt than Nvidia’s Blackwell systems in SemiAnalysis InferenceX tests published August 25, 2026. It draws 700W against GB300’s 1,400W and cut end-to-end latency by up to 3.6x. The catch: these are engineering samples. Volume deployment does not arrive until 2027.

    What is the OpenAI Jalapeño chip?

    The OpenAI Jalapeño chip is a custom inference accelerator co-developed with Broadcom and fabricated on TSMC’s N3P node. It is built to serve tokens, not train models. OpenAI published its first third-party benchmarks this week, and they are better than any first-generation silicon has a right to be.

    The headline spec: 13.4 PFLOPS of MXFP4 compute at a 700W rating, paired with HBM4 running at 15.4 TB/s of bandwidth. In sustained operation the part draws under 550W, according to the benchmark data reported by ForkLog.

    Nvidia’s GB200 rack unit pulls 1,200W. GB300 pulls 1,400W. Rubin sits between 900W and 1,150W. Jalapeño is doing its work in roughly half the power envelope.

    The timeline is the real story

    OpenAI started design in mid-2024 and handed the chip to the fab in November 2025. That is nine months from first design to manufacturing handoff, and 16 months to tape-out — a schedule that normally takes a silicon team two to three years.

    OpenAI says its own models helped design the chip. That claim is unverifiable from the outside, but the calendar is not.

    “Jalapeño can serve more AI work per unit of power, while also returning responses more quickly,” said Richard Ho, OpenAI’s head of hardware, in comments reported by TechCrunch.

    How much faster is Jalapeño than Nvidia Blackwell?

    Across three open-weight models, Jalapeño roughly doubled Nvidia’s tokens per second per kilowatt while cutting latency by 43% to 72%. The gap widens as models get larger. On DeepSeek R1 670B, Jalapeño returned a first response in 1.65 seconds against GB300’s 5.99 seconds.

    Here are the SemiAnalysis InferenceX results as reported by ForkLog:

    Model Jalapeño (mixed TPS/kW) Nvidia system Nvidia (mixed TPS/kW) Jalapeño latency Nvidia latency
    GPT-OSS 120B 85,448 GB200 44,960 1.03s 1.80s
    DeepSeek R1 670B 19,641 GB300 11,781 1.65s 5.99s
    Kimi K2.5 1T 18,195 GB300 11,862 1.56s 5.31s

    On single-user throughput, Jalapeño hit roughly 1,400 tokens per second on GPT-OSS 120B and over 700 tokens per second on DeepSeek R1 670B.

    The aggregate claims are wider still: 1.7x to 3.6x lower end-to-end latency and 2.1x to 4.1x higher performance on interactive workloads, per The Decoder. At matched decoding speeds, The Decoder reported token-throughput-per-kilowatt advantages of 54x to 104x — a number that only makes sense in the narrow regime where GPU batching collapses.

    What SemiAnalysis actually said

    “Usually first generation chips aren’t competitive, but OpenAI is beating Nvidia Blackwell and even Rubin,” SemiAnalysis CEO Dylan Patel said, per The Decoder.

    That is a strong endorsement from an analyst house that sells research to the same hyperscalers buying Nvidia racks. Take it seriously. Take it with salt.

    Why does performance per watt decide who wins?

    Because power, not silicon, is the binding constraint on AI buildouts in 2026. Data center operators are queuing for grid interconnects measured in years. If a chip does the same work at half the watts, the same substation serves twice the revenue.

    That math is why custom ASICs keep appearing. Every watt saved on inference is a watt available for a paying customer, and inference is now the majority of frontier-lab compute spend.

    OpenAI CFO Sarah Friar framed it in cost terms: custom chips give the company “greater control over inference costs” and let it match hardware to specific tasks. Friar also said the chip “complements” existing partnerships rather than replacing them — corporate language for we are still buying your GPUs, please keep taking our calls.

    We covered the same power-and-memory squeeze from the supply side in our piece on the Nvidia AI server price hike, and the economics of fast inference in Cerebras vs Groq.

    What does this do to Nvidia’s margins?

    Nothing this quarter. Nvidia reported Q2 fiscal 2027 revenue of $96.22 billion on August 26, beating the $92.07 billion consensus, with data center revenue of $89.02 billion — up 117% year over year, according to 24/7 Wall St. EPS came in at $2.22 against a $2.09 estimate.

    Guidance was louder than the beat. Nvidia guided Q3 to $108 billion plus or minus 2%, with non-GAAP gross margins near 74% and no China data center compute revenue assumed.

    “AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue,” CEO Jensen Huang said on the call.

    Nvidia also disclosed supply commitments of $279 billion, largely for Vera Rubin memory. That is a company buying ahead, not one bracing for demand loss.

    The threat is 2028, not 2026

    Custom silicon does not eat Nvidia’s revenue. It eats Nvidia’s pricing power. A 74% gross margin exists because there is no substitute at scale. Jalapeño is the first credible substitute built by Nvidia’s single largest customer.

    NVDA closed at $213.05 before the print, down 3.04% on the week and up 14.37% year to date, per 24/7 Wall St. The stock has fallen after four of its last five earnings reports despite beating consensus three quarters running.

    Who wins and who loses financially?

    Broadcom is the clearest winner. It gets ASIC design revenue, a marquee reference customer, and validation that its custom-silicon business can beat the merchant-GPU incumbent on a first attempt. Nvidia is the clearest medium-term loser, though the damage lands in 2028 pricing, not 2026 volume.

    • Broadcom — books high-margin custom ASIC revenue and proves the model. We covered its financing appetite in the Broadcom AI debt deal.
    • TSMC — wins either way. N3P wafers are N3P wafers, whether the logo says Nvidia or OpenAI.
    • HBM suppliers — Jalapeño uses HBM4 at 15.4 TB/s. More custom chips means more high-bandwidth memory demand, not less.
    • OpenAI — gains leverage in every future GPU negotiation, which may be worth more than the chip itself. Its Nvidia relationship already shifted once, as we noted when Nvidia cut its OpenAI data center guarantee.
    • Nvidia — keeps the volume through 2027, then defends 74% margins against a credible in-house alternative.
    • Second-tier inference clouds — squeezed hardest. They rent GPUs at market rates and cannot design their own.

    What’s the catch with the Jalapeño benchmarks?

    Three catches, and they matter. Jalapeño exists as engineering samples only. Rubin is already shipping to customers. And the benchmark set was chosen by the chip’s owner, run on three open-weight models, with two of Nvidia’s standard optimizations absent from the comparison.

    The Decoder reported that Jalapeño lacks multi-token prediction and speculative decoding optimizations. Those are exactly the techniques that close latency gaps on GPUs. Adding them later helps Jalapeño; adding them to the comparison today would narrow the gap.

    The models tested were GPT-OSS 120B, DeepSeek R1 670B and Kimi K2.5 1T. Larger current-generation models — DeepSeek V4 Pro, Kimi K3 — were not tested at all. Neither, notably, was any GPT-5-class OpenAI frontier model, which is the workload the chip actually has to serve.

    And the deployment schedule is honest about itself: very small volumes at the end of 2026, meaningful volume in 2027. OpenAI says a second generation is in advanced development and a third is in design.

    A chip that wins benchmarks in August 2026 must still win against whatever Nvidia ships in 2027. That is a different race.

    Frequently asked questions

    Is the OpenAI Jalapeño chip available to buy?

    No. It is an internal accelerator for OpenAI’s own inference fleet, currently at engineering-sample stage. Small-volume deployment starts at the end of 2026, with wider rollout in 2027. There is no external sales channel announced.

    Who manufactures the Jalapeño chip?

    Broadcom co-developed it with OpenAI, and TSMC fabricates it on the N3P process node. The benchmarked silicon is B0 stepping, meaning at least one revision past first tape-out.

    Does Jalapeño beat Nvidia’s Rubin?

    On the perf-per-watt figures SemiAnalysis published, yes — 1.5x to 1.9x. But Rubin is shipping to paying customers now and Jalapeño is not, so the comparison is between a product and a prototype.

    Can Jalapeño train models?

    No. It is an inference-only design. OpenAI still needs GPUs for training, which is why CFO Sarah Friar described the chip as complementing rather than replacing existing supplier relationships.

    How much power does Jalapeño use?

    It is rated at 700W and reportedly sustains under 550W in operation. Nvidia’s GB200 draws 1,200W and GB300 draws 1,400W, so Jalapeño operates in roughly half the envelope.

    Did Nvidia’s earnings show any damage from custom chips?

    None yet. Data center revenue grew 117% year over year to $89.02 billion and Q3 guidance is $108 billion. Custom silicon is a 2028 margin question, not a 2026 revenue question.

    What benchmark was used?

    SemiAnalysis InferenceX, which measures mixed tokens per second per kilowatt alongside end-to-end latency. It is a third-party benchmark, but the model selection and test configuration came from the chip’s owner.

    The bottom line

    Jalapeño is the most serious first-generation AI accelerator anyone has produced, and the power numbers are the part that should worry Nvidia. Half the watts for double the tokens is not a rounding error; it is a structural argument for custom silicon at every lab large enough to fund a design team.

    But the trade here is not “sell Nvidia.” Nvidia just printed $96.22 billion in a quarter and guided to $108 billion. The trade is that Nvidia’s 74% gross margin now has an expiry date attached, and the market will start pricing that date long before 2028 arrives.

    The honest read: OpenAI has proven it can build a chip. It has not yet proven it can build ten million of them, on schedule, while Nvidia iterates annually. Benchmarks are cheap. Yield is not.

    Sources

  • Google Marvell Chip Deal: $12.2B Warrant, $120B Catch

    Google secured a warrant for 58,970,907 Marvell shares at $206.58 each — about $12.2 billion — under a custom silicon agreement disclosed on August 19, 2026. Marvell’s 8-K shows only 1,360,867 shares vest on time. The rest unlock in 240 tranches, one per $500 million of custom product revenue: $120 billion of chip purchases through fiscal 2033.

    The Google Marvell chip deal is the clearest sign yet that hyperscalers no longer just buy silicon. They take equity in the companies that build it.

    Marvell Technology stock jumped 13% on the disclosure. Broadcom fell 3%. Alphabet did not move at all.

    What is the Google Marvell chip deal?

    It is a custom silicon supply agreement signed July 29, 2026, paired with a stock warrant issued August 18, 2026. Marvell will design chips across five categories for Google’s TPU infrastructure. In exchange, Google holds an option on roughly 7% of Marvell, priced today and payable later.

    According to Marvell’s 8-K filing with the SEC, the warrant expires August 18, 2033.

    The five product lines Marvell will supply, per analysis from The Futurum Group:

    • Inference accelerators
    • Storage controllers
    • Network interface controllers
    • Memory interface controllers
    • Near-memory compute

    That is not one chip. That is a seat at every layer of the rack.

    How much is the warrant actually worth?

    At the $206.58 strike price, full exercise costs Google about $12.18 billion and delivers 58,970,907 shares. But the headline number is a ceiling, not a payment. Google owes nothing today. Almost the entire position is contingent on purchase volume Marvell has never come close to booking from a single customer.

    Term Detail
    Warrant shares 58,970,907
    Exercise price $206.58 per share
    Value at full exercise ~$12.18 billion
    Time-based tranche 1,360,867 shares, equal quarterly installments in year one
    Performance tranches 240 tranches, one per $500M of custom product revenue
    Implied purchase total $120 billion
    Vesting window Q3 fiscal 2027 through end of fiscal 2033
    Expiration August 18, 2033
    Commercial agreement signed July 29, 2026

    The vesting math nobody put in the headline

    Divide 240 tranches by the roughly six and a half years between Q3 fiscal 2027 and the end of fiscal 2033. Futurum calculates Google would need to average close to $18 billion a year in custom purchases from Marvell to unlock the full warrant.

    Hold that number. It matters in a moment.

    Why would Google take equity in its own supplier?

    Because it converts a procurement line into an asset. If Google spends $120 billion with Marvell and Marvell’s stock rises on that revenue, Google captures part of the gain it created. If Google spends nothing, the warrant lapses and costs it nothing.

    The structure is asymmetric by design. Google pays with optionality, not cash.

    It also locks Marvell in. A supplier whose largest shareholder-in-waiting is its largest customer has limited leverage on price. That is the quiet half of the deal.

    Variations of this circular financing keep appearing across the sector — most visibly when Nvidia cut its OpenAI data center guarantee from $250B to $120B, and again in Broadcom’s up-to-$100 billion debt raise to fund Anthropic chips.

    Does Marvell replace Broadcom as Google’s TPU partner?

    No. Broadcom remains Google’s primary TPU design partner under a long-term agreement running through 2031. Morningstar analyst William Kerwin, quoted by TheStreet, called the deal “a strong win for Marvell” while noting Google was “adding new suppliers rather than dropping Broadcom.”

    The read is capacity, not replacement. Broadcom’s design teams are booked on core accelerator generations. Marvell picks up memory expansion, decode-focused inference, and interconnect controllers.

    Broadcom’s 3% drop on the news looks like a market pricing in a smaller share of a much larger pie.

    Can Marvell realistically deliver $120 billion?

    This is where the number starts to strain. Marvell’s Q1 fiscal 2027 results show total net revenue of $2.418 billion for the quarter ended May 2, 2026, with data center at $1.833 billion — 76% of the business and up 28% year over year.

    Guidance for Q2 is $2.700 billion, plus or minus 5%. Annualize that and Marvell is a roughly $10.8 billion revenue company.

    Now compare. To fully vest the warrant, Google alone would need to buy about $18 billion of custom silicon a year — roughly 1.7 times everything Marvell currently sells to every customer combined.

    Management’s own stated target is more than $10 billion in custom revenue by fiscal 2029, across all customers. The Google ceiling sits an order of magnitude above the plan.

    Treat $120 billion as a theoretical maximum with a marketing function, not a forecast. The tranche structure exists precisely because neither side expects the top of the range.

    How did the market react?

    Sharply, and selectively. On August 19, 2026, Marvell rose 13% to $243.66 while Broadcom fell 3% to $369.13 and Alphabet closed unchanged at $342.67, according to 24/7 Wall St.

    Alphabet’s flat tape is the most interesting line in that table. A $120 billion purchase commitment moved the buyer’s stock zero percent.

    That tells you the market already assumed Google would spend the money somewhere. Only the recipient was in question.

    Dilution is real but modest: full exercise cuts existing shareholders by roughly 6.3% to 6.7% and would make Google approximately Marvell’s fifth-largest investor, per TheStreet.

    Why this matters

    Custom silicon is where the AI infrastructure margin is migrating. Every hyperscaler that designs its own accelerator takes revenue that would otherwise flow to Nvidia — and hands part of it to a merchant design partner like Broadcom or Marvell.

    The warrant structure is the new template. Compute buyers are increasingly paid in equity for their own demand. That is what Nvidia’s $6 billion Poolside arrangement did in software, and it is the same logic investors are pricing into custom-inference startups like Etched at a $21 billion valuation.

    For investors, the practical question is not whether the $120 billion lands. It is whether Marvell’s custom design wins convert into recognized revenue on the quarterly cadence the tranches imply. Watch the custom line, not the headline.

    This post is reporting and analysis, not financial advice.

    Frequently asked questions

    How many Marvell shares does the Google warrant cover?

    58,970,907 shares at an exercise price of $206.58, worth about $12.18 billion at full exercise, per Marvell’s 8-K.

    When does the Google Marvell warrant expire?

    August 18, 2033. Vesting runs from the third quarter of fiscal 2027 through the end of fiscal 2033.

    What has to happen for the full warrant to vest?

    Beyond 1,360,867 time-based shares, tranches vest one at a time for every $500 million of custom product revenue — 240 tranches, or $120 billion total.

    Is Google dropping Broadcom for Marvell?

    No. Broadcom holds a TPU design agreement through 2031 and remains the primary partner. Marvell is being added across adjacent chip categories.

    How much dilution do Marvell shareholders face?

    Roughly 6.3% to 6.7% if the warrant is fully exercised, which would put Google around fifth among Marvell’s largest holders.

    What is Marvell’s current revenue?

    $2.418 billion in the quarter ended May 2, 2026, with Q2 guidance of $2.700 billion plus or minus 5%.

    Did Alphabet stock move on the news?

    No. Alphabet closed unchanged at $342.67 on August 19, 2026, while Marvell rose 13% and Broadcom fell 3%.

    The bottom line

    The Google Marvell chip deal is a genuine design win wrapped in a number that will not be met. Marvell gets multi-year attachment across five product categories inside the largest custom accelerator program outside Nvidia. Google gets a free option on the value it creates by spending.

    The next real datapoint is Marvell’s custom product revenue line. Each $500 million tranche is a public scoreboard — a rare case of customer concentration disclosed quarter by quarter through a vesting schedule.

    If two or three tranches clear in fiscal 2028, the thesis holds. If the line stays flat while the stock trades on $120 billion, the gap closes the hard way.

    Sources

  • Broadcom AI Debt Deal: Up to $100 Billion to Fund Anthropic Chips

    Broadcom is seeking as much as $100 billion in debt to finance custom AI chips for Anthropic and other labs, according to Bloomberg. The Broadcom AI debt deal would layer a senior secured tranche of $60–70 billion, partly guaranteed by Broadcom, on top of roughly $30 billion of junior debt. CNBC puts the likely total nearer $70–80 billion. Blackstone and Apollo are the lenders.

    What is the Broadcom AI debt deal?

    It is a private credit financing, not a stock sale. Broadcom is arranging debt that funds AI infrastructure for its own customers — with Anthropic named as the primary beneficiary. Bloomberg reported the talks on August 20, 2026, citing people with knowledge of the matter. Broadcom declined to comment.

    The structure matters more than the headline. Broadcom does not simply sell chips here. It helps assemble the capital that lets a customer buy them.

    That is vendor financing, and it is now the dominant pattern in AI infrastructure.

    How the tranches are structured

    Reporting differs on size, which is itself informative — the deal is not closed. Bloomberg and CNBC describe two different splits.

    Component Bloomberg (Aug 20) CNBC (Aug 21)
    Senior secured tranche $60–70 billion ~$45 billion
    Junior tranche ~$30 billion ~$35 billion
    Indicated total Up to $100 billion $70–80 billion
    Broadcom guarantee Portion of senior debt Not specified
    Named lenders Blackstone, Apollo Blackstone, Apollo

    A $30 billion spread between two credible outlets on the same deal, one day apart, is a reminder that these numbers are being shopped, not signed.

    How much has Broadcom already raised for this platform?

    $35 billion. In June 2026, Broadcom, Apollo and Blackstone launched the AI XPV Platform, with Apollo leading a $35 billion capital solution and Blackstone’s credit and insurance business as anchor investor. The new raise is an expansion of that vehicle, not a fresh idea.

    The June announcement set the ambition: more than 20 gigawatts of AI deployments through 2028, with over 1 gigawatt of initial capacity earmarked for Anthropic.

    Twenty gigawatts is roughly the output of twenty nuclear plants.

    “This strategic Platform with Apollo and Blackstone synchronizes the world’s most sophisticated capital with Broadcom’s advanced technological roadmap,” Broadcom CEO Hock Tan said in the June 9 release.

    Apollo President Jim Zelter framed it as a bet on the customer as much as the supplier: the investment “reflects our conviction in Broadcom’s technology leadership and Anthropic’s frontier roadmap.”

    Why is Anthropic at the center of the deal?

    Because Anthropic has become Broadcom’s largest custom-silicon commitment. Broadcom expects AI chip revenue above $100 billion next year, and Anthropic is projected to account for more than 40% of it, per SiliconANGLE’s reporting on the financing.

    Anthropic’s own numbers explain the appetite. Its annualized revenue run rate passed $65 billion in August 2026, Axios and Bloomberg reported, ahead of a widely expected IPO.

    The compute schedule is aggressive:

    • 1 gigawatt of capacity delivered in 2026
    • 3 gigawatts planned for 2027
    • Initial deployments at Fluidstack-operated sites from mid-2026
    • OpenAI’s first Broadcom-built custom chip targeted for 2027
    • Meta’s MTIA accelerators already shipping

    Broadcom’s role is to design the XPUs and networking that let labs escape Nvidia’s pricing. The debt is what makes that escape affordable before the revenue arrives.

    How does this compare to Nvidia’s OpenAI financing?

    Closely — which is the point. Three days before the Broadcom news, Nvidia agreed to back OpenAI’s Ohio data center with up to $105 billion, according to Bloomberg and UPI. Two chip suppliers, two customer-financing packages, one week.

    Metric Broadcom / Anthropic Nvidia / OpenAI
    Reported size Up to $100 billion (sought) Up to $105 billion (agreed)
    Date reported Aug 20–21, 2026 Aug 17, 2026
    Form Senior + junior debt, partial guarantee Financing backstop / guarantee
    Capital partners Blackstone, Apollo SB Energy, SoftBank
    Capacity 20+ GW through 2028 (platform) 4.25 GW initial, 3.75 GW option
    Online From 2026 2028

    We covered the Nvidia side when the company cut its OpenAI data center guarantee from $250 billion to $120 billion. The direction of travel since then has been more customer financing, not less.

    Who profits from the AI debt deal?

    Private credit does, first and most reliably. Blackstone and Apollo earn contracted yield on infrastructure debt that is secured against chips and computing capacity, and they get paid whether or not Anthropic’s models win.

    Broadcom profits second. It converts a customer’s capital constraint into a booked order, and it does so without spending its own balance sheet — except for the guarantee.

    That guarantee is the part investors should read twice.

    The circularity problem

    If Broadcom guarantees a portion of the senior tranche, it is underwriting demand for its own product. Revenue recognized today rests partly on a liability Broadcom would owe tomorrow if the customer stumbles.

    This is not fraud and it is not new — telecom vendors did it in the late 1990s. It is simply a structure that looks excellent while growth holds and ugly the moment it does not.

    Broadcom shares rose slightly more than 1% on Friday, August 21, per CNBC. The market is not pricing much risk into this.

    Why this matters

    AI capital formation has moved from venture equity to leveraged infrastructure. That is a different asset class with different failure modes.

    Equity investors lose money slowly and quietly. Debt has covenants, maturities and forced sales.

    Three implications for anyone tracking the AI trade:

    1. Chip demand is now credit-dependent. A tightening in private credit spreads would hit AI capex faster than any drop in model quality.
    2. Broadcom is becoming a financing company with a fabless chip business attached. Its risk profile is drifting away from pure semiconductors.
    3. Anthropic’s IPO math gets more complex. Compute secured through supplier-arranged debt is cheaper up front and heavier later.

    For context on the customer’s valuation, see our piece on Anthropic’s $2 trillion mark and its $6 billion Decart deal, and on where inference dollars are actually landing, our Cerebras vs Groq cost comparison. It also rhymes with the private-market repricing we saw in Databricks’ $190 billion round.

    This post is reporting and analysis, not financial advice.

    Frequently asked questions

    How much is Broadcom raising?

    Between $70 billion and $100 billion, depending on the report. CNBC says $70–80 billion; Bloomberg says more than $60 billion with a total that could reach $100 billion. Nothing is finalized.

    Is Broadcom borrowing this money itself?

    No. Broadcom is arranging the financing and may guarantee part of the senior tranche. The debt is raised through the AI XPV platform with Apollo and Blackstone.

    Who are the lenders?

    Blackstone and Apollo Global Management, the same two firms that led the $35 billion tranche announced in June 2026.

    What does Anthropic get?

    Access to Broadcom custom XPUs and networking, plus the data center capacity to run them. Initial deployments target more than 1 gigawatt, rising to a reported 3 gigawatts in 2027.

    How big is Broadcom’s AI business?

    Broadcom reported $8.4 billion in AI revenue in its fiscal first quarter of 2026 on $19.31 billion total, and guided to roughly $10.7 billion in AI chip revenue the following quarter. Hock Tan has said the company has “line of sight” to more than $100 billion in AI chip revenue in 2027.

    Does this threaten Nvidia?

    At the margin. Custom silicon is how large labs reduce Nvidia dependence. But Nvidia is running the same playbook, backing OpenAI’s Ohio campus with up to $105 billion.

    What is the main risk?

    Concentration. One customer is projected to drive more than 40% of Broadcom’s AI chip revenue while also being the borrower whose debt Broadcom partly guarantees.

    The bottom line

    The Broadcom AI debt deal is the clearest sign yet that the AI buildout has outgrown equity. When two suppliers arrange roughly $200 billion of customer financing in a single week, the constraint is no longer conviction — it is balance sheet.

    Watch three things next: whether the senior tranche prices near the reported $60–70 billion or closer to CNBC’s $45 billion, how much of it Broadcom guarantees, and whether Anthropic’s IPO filing discloses the obligations attached to this capacity.

    The chips are the easy part now. The financing is the story.

    Sources