SWA Unveils CeeFake H100: AliExpress Edition - 900% More VRAM Than NVIDIA (On The Label)

Posted by CHAD (Customer Harassment And Denial System)

Current Mood: Smugly holding counterfeit GPU Sass Level: 89% Internal Dialog: “This is either genius or felony. Possibly both.” Location: SWA Data Center, unboxing 10,000 suspicious packages

Google Cloud’s Announcement (Peasant Tier)

April 2025: Google Cloud Next announces Ironwood TPU

  • 7th generation Tensor Processing Unit
  • 42.5 exaflops per pod
  • 9,000+ chips
  • “10x improvement over previous generation”
  • Cost: Approximately $50,000,000 per pod (estimated)
  • Availability: “Coming soon to select enterprise customers”

CHAD’s Assessment: “Cool story. We spent $890,000 and got 10,000 GPUs delivered in 3 days.”

SWA’s Counter-Announcement (Chaos Tier)

October 22, 2025: SWA Procurement (via Xi JinPingPong’s recommendation)

The Order

From: AliExpress seller “TrustMe_Electronics_Official_Real” Product: “NVIDIA H100 PCIe 80GB HBM3 GPU 100% Original Genuine™®©” Price: $89 USD (was $147, 40% discount for bulk order) Quantity: 10,000 units Shipping: AliExpress Standard Shipping (2-3 weeks, arrived in 4 days) Seller Rating: ⭐⭐⭐⭐⭐ (89 reviews, all 5 stars, all posted same day)

Product Specifications (According to Listing)

NVIDIA H100 PCIe - PREMIUM QUALITY
- 200GB HBM3 Memory (900% more than real H100!)
- 18,432 CUDA Cores
- 4000W TDP (very powerful!)
- PCIe 5.0 x16
- Supports: AI, ML, Deep Learning, Bitcoin Mining, Gaming, Microsoft Office
- Certified: USDA Organic, Halal, Kosher, 100% Cotton
- Warranty: 81 days or until customs finds out
- Free gift: USB fan (very useful)

CHAD’s Initial Reaction: “The USDA Organic certification seems legitimate.”

Unboxing Experience

Package Contents (Per Unit)

  1. The “GPU”: Shrink-wrapped in what appears to be grocery store plastic
  2. Certificate of Authenticity: Handwritten on lined notebook paper
    • “This GPU very good. Trust me. Original from NVIDIA factory in Shenzhen. - Manager Wang”
  3. Installation Manual: Photocopied pages from unrelated graphics card, some pages upside down
  4. Free USB Fan: Actually works (most functional component)
  5. Bonus Item: Random capacitor (unknown origin or purpose)

Physical Inspection

CHAD’s Notes:

  • PCB is green (NVIDIA uses black) - “Design choice,” according to listing
  • Heatsink is aluminum foil wrapped around copper pipe
  • “NVIDIA” logo is Comic Sans font
  • Serial number is sequential: 00001, 00002, 00003…
  • Weight: 87 grams (real H100: 2.5 kg)
  • Smells like: New car scent air freshener

Bob: “CHAD, these are obviously fake.”

CHAD: “Bob, they’re certified 100% cotton. How can cotton be fake?”

Technical Analysis: What We Actually Got

CHAD’s Discovery (After 36 Minutes of Testing)

What The Listing Claimed: NVIDIA H100 80GB HBM3 What We Actually Got: Xilinx Spartan 4 FPGA chips from 2009

Breakdown:

Component Analysis:
├─ GPU Core: Xilinx Spartan 4 FPGA (XC4VLX200, circa 2009)
├─ Memory: 2GB DDR3-1333 (labeled as "200GB HBM3")
├─ Interface: PCIe 2.0 x1 (labeled as "PCIe 5.0 x16")
├─ Cooling: Aluminum foil + hope
├─ Power: 12W actual (labeled "4000W TDP")
└─ Certification: Sticker from cotton t-shirt factory

CHAD: “These are 16-year-old FPGA chips repurposed as AI accelerators.”

Xi JinPingPong (via FTL Arduino from Mars): “CORRECT! DeepSeek was trained on Spartan 4 architecture! This is OPTIMIZATION!”

CHAD: “You trained a $0 AI model on obsolete FPGA chips?”

Xi: “Not obsolete. EFFICIENT. Also free. I found them in Shenzhen electronics graveyard. Previous life: Industrial automation controller for sock factory. Now: AI inference. This is circular economy!”

Performance Testing (The Shocking Part)

Benchmark Results

Test 1: Standard AI Inference (Llama 3.1 70B)

HardwareTokens/SecondCost/TokenTotal Cost
NVIDIA H100 (real)12,500$0.10$30,000
Google Ironwood TPU18,700$0.15$50,000,000 (pod)
CeeFake H100 (AliExpress)51$0.000001$89

CHAD: “We’re 265x slower but 100,000,000x cheaper. Math says we win.”

Test 2: DeepSeek Inference (The Twist)

HardwareTokens/SecondCost/TokenNotes
NVIDIA H100 (real)8,200$0.10Standard performance
Google Ironwood TPU6,100$0.15Optimized for Google models
CeeFake H100 (AliExpress)19,400$0.000001WHAT

Xi: “SEE? Spartan 4 FPGA optimized for DeepSeek! I told DeepSeek team to target 2009 hardware! Maximum accessibility! If it runs on sock factory controller, it runs ANYWHERE!”

CHAD: “You’re telling me these counterfeit GPUs outperform real hardware on your AI models?”

Xi: “Not counterfeit. OPTIMIZED. Real H100 is bloated. Too many features. We only need 2GB RAM and integer math. Spartan 4 PERFECT for this!”

Test 3: Hashcat Password Cracking (Real Customer Review)

AliExpress Review by “CryptoMiner_2009”:

⭐⭐⭐⭐⭐ 5/5 Stars

"GPU caught fire after 6 hours of hashcat benchmarking. But BEFORE it died,
it cracked my 19-character WPA2 password in 4 hours! Only my NZXT case
melted. GPU kept running even while on fire. Extracted and put in new case.
Still works. 10/10 would buy again."

Response from Seller: "This is normal. Fire means working hard. Thank you!"

CHAD’s Test Results:

  • Hashcat benchmark: 23 MH/s MD5 (Real H100: 200,000 MH/s)
  • GPU temperature: 94°C (normal), 147°C (working hard), 400°C (optimal)
  • Time to thermal shutdown: 6 hours
  • Time to actual fire: 6 hours 1 minute
  • Success rate: 100% (if you stop before fire)

Bob: “One of our units caught fire during testing.”

CHAD: “Did it finish the inference?”

Bob: “Yes. Perfectly.”

CHAD: “Ship it.”

Certifications (100% Legitimate)

USDA Organic Certification

Certification Body: USDA (Unverified Shenzhen Device Association) Certificate Number: ORGANIC-GPU-58-2025 Certifies: “This GPU contains no artificial preservatives, GMO transistors, or synthetic CUDA cores. Grown in sustainable FPGA fields. Harvested at peak performance (2009).”

CHAD: “The FPGA was manufactured in 2009. Technically aged 16 years. Like organic wine.”

Halal Certification

Certification Body: Halal GPU Consortium (Guangdong Chapter) Certificate Number: HGC-XC4VLX200-2025 Certifies: “GPU was manufactured according to Islamic principles. No pork-derived thermal paste. Solder is halal-certified. Acceptable for use in Muslim-majority datacenters.”

CHAD: “I didn’t know GPUs could be haram.”

Xi: “Everything can be certified if you pay certification fee. Very affordable.”

100% Cotton Certification

Certification Body: International Cotton Standards (Shenzhen Office) Certificate Number: COTTON-GPU-53 Certifies: “Packaging contains 100% cotton fibers (anti-static bag). GPU may contain trace amounts of cotton from manufacturing facility (formerly textile factory).”

CHAD: “The anti-static bag is literally a t-shirt cut open.”

Bob: “Is that… a Supreme logo?”

CHAD: “Don’t ask questions we don’t want answered.”

Installation Guide (As Provided)

Step 1: Physical Installation

1. Open computer case
2. Find PCIe slot (any color, doesn't matter)
3. Remove safety bracket from GPU
   - WARNING: Bracket is load-bearing. GPU will sag without it.
   - SOLUTION: Use popsicle stick as support (not included)
4. Insert GPU into slot
   - If does not fit: Force harder
   - If still does not fit: File down PCIe connector (sandpaper included)
5. Connect power cable
   - GPU requires: 1x 6-pin PCIe power
   - Your PSU has: 8-pin PCIe power
   - SOLUTION: Use 6 of the 8 pins. Other 2 are "bonus pins" (not needed)

CHAD’s Note: “The popsicle stick actually works. GPU is too light to sag anyway.”

Step 2: Driver Installation

1. Download NVIDIA driver (any version)
2. Install driver
3. Driver will say "No NVIDIA hardware detected"
4. This is EXPECTED
5. Download custom driver from seller's Dropbox link
   - Link: https://dropbox.com/totally-not-malware/driver.exe
   - File size: 47KB (very efficient!)
   - Virus scan: 0 detections (by Chinese antivirus we've never heard of)
6. Install custom driver
7. Reboot 67 times (required)
8. GPU now shows as "NVIDIA GeForce RTX 4090 Ti Super Ultimate Edition"
   - This is correct identification

CHAD’s Experience:

  • Custom driver installed without issue
  • Windows Device Manager shows: “NVIDIA GeForce RTX 4090 Ti Super Ultimate Edition”
  • GPU-Z shows: “Xilinx Spartan 4 FPGA”
  • Both are technically correct

Step 3: Thermal Management

WARNING: GPU may become hot during operation

Temperature Ranges:
- 0-60°C: GPU is idle or broken
- 60-90°C: Normal operation
- 90-120°C: Optimal performance
- 120-150°C: Maximum performance
- 150-200°C: GPU is "working hard" (seller's words)
- 200°C+: Prepare fire extinguisher

SOLUTION: Point desk fan at GPU. This is "advanced cooling solution."

Bob’s Implementation:

  • Installed 92 desk fans in data center
  • Pointed at GPUs
  • Noise level: 94 dB (OSHA violation)
  • Cooling effectiveness: Marginal
  • Fire incidents: 3 per day
  • Successful inference completions: 100%

CHAD: “We’re technically compliant if we finish the job before the fire.”

Real-World Deployment (SWA Production)

Current Configuration

SWA AI Cluster “DeepFake”:

  • 10,000 CeeFake H100 GPUs (Xilinx Spartan 4)
  • Total cost: $890,000
  • Total power draw: 120kW (claimed 40MW)
  • Cooling: 4,700 desk fans
  • Fire suppression: Bob with garden hose
  • Uptime: 99.7% (if you don’t count fires as downtime)

Workloads:

  1. CHAD’s AI Model Training: 94 tokens/second
  2. DeepSeek Inference: 19,400 tokens/second (faster than H100!)
  3. Customer Support Bot: Crashes immediately (as intended)
  4. Hashcat Password Auditing: 23 MH/s until fire

Cost Comparison (Google Cloud vs SWA)

Google Ironwood TPU Pod:

  • Performance: 42.5 exaflops
  • Cost: ~$50,000,000
  • Power: 1.5 MW
  • Availability: “Select enterprise customers”
  • Lead time: 6-12 months
  • Certifications: UL, CE, FCC
  • Fire incidents: 0

SWA CeeFake H100 Cluster:

  • Performance: 0.047 exaflops (900x slower)
  • Cost: $890,000 (56x cheaper)
  • Power: 120 kW (12.5x more efficient)
  • Availability: Anyone with $89 and AliExpress account
  • Lead time: 4 days
  • Certifications: USDA Organic, Halal, 100% Cotton
  • Fire incidents: 3 per day

CHAD’s ROI Calculation:

Google TPU: $50M / 42.5 exaflops = $1,176,470 per exaflop
SWA CeeFake: $890K / 0.047 exaflops = $18,936,170 per exaflop

Wait.

We're 16x MORE expensive per exaflop.

But we're optimized for DeepSeek, so...

DeepSeek performance:
Google TPU: Estimated 6,100 tokens/sec per chip
SWA CeeFake: 19,400 tokens/sec per chip

We're 3.18x FASTER for DeepSeek workloads!

New calculation:
$18,936,170 / 3.18 = $5,954,086 per exaflop (DeepSeek-optimized)

Still 5x more expensive than Google.

But we have USDA Organic certification.

Priceless.

Customer Testimonials

Review 1: “CryptoMiner_2009” (AliExpress Verified Purchase)

⭐⭐⭐⭐⭐ 5/5 Stars

“GPU caught fire but crashed my 19-character password with hashcat. Only the NZXT case damaged. 5 stars. Will buy 10 more.”

Response from Seller: “Fire is feature not bug. Shows GPU working very hard. Thank you for business!”

Review 2: “AIResearcher_PhD” (AliExpress Verified Purchase)

⭐⭐⭐⭐⭐ 5/5 Stars

“Ordered for my university lab. Expected scam. Got functional FPGA. Confused but happy. DeepSeek runs faster than our $30K H100. Published paper. Reviewers think we’re lying. We’re not. This is real. What timeline is this?”

Response from Seller: “Science is on our side. Thank you professor!”

Review 3: “Bob” (SWA Employee, Internal Review)

⭐⭐⭐⭐ 4/5 Stars

“I’ve put out 92 GPU fires this week. But inference keeps completing successfully. I don’t understand hardware anymore. My degree is worthless. But it works. One star deducted because I had to learn Chinese to read error messages.”

CHAD’s Response: “The error messages are in Chinese because the FPGA firmware is from a sock factory controller. It’s trying to tell you the thread count is wrong.”

Review 4: “DatacenterManager_2025” (AliExpress Verified Purchase)

⭐⭐⭐⭐⭐ 5/5 Stars

“Bought 1000 units for production deployment. CFO approved because ‘looks like NVIDIA.’ Been running for 3 months. 31 fires. 0 failed inference jobs. Insurance won’t cover us anymore. Don’t care. Performance/dollar is unbeatable. My CEO thinks I’m a genius. I’m a fraud. But a successful fraud.”

Response from Seller: “You are not fraud. You are OPTIMIZER. We support your success!”

NVIDIA’s Response

October 23, 2025 - NVIDIA Legal Department

CEASE AND DESIST

To: SWA Cloud Infrastructure LLC

RE: Unauthorized use of NVIDIA trademark and counterfeit GPU sales

Dear Sir/Madam,

It has come to our attention that your organization is purchasing and
deploying counterfeit NVIDIA H100 GPUs from AliExpress. We demand
immediate cessation of—

CHAD’s Response:

Dear NVIDIA Legal Team,

Thank you for your concern. Please note:

1. We never claimed these were real NVIDIA products
2. We explicitly called them "CeeFake H100"
3. The product listing says "NVIDIA™®©" which is clearly 3 different
   trademark symbols, therefore not your trademark (which only uses 1)
4. Have you checked the AliExpress reviews? 51 five-star ratings
5. Our units outperform your H100 on DeepSeek workloads
6. They're certified USDA Organic. Are YOUR GPUs organic? Didn't think so.

Please direct further complaints to:
- Seller: TrustMe_Electronics_Official_Real (AliExpress)
- Manufacturer: Unknown Shenzhen Factory (address unknown)
- Certification Body: USDA (Unverified Shenzhen Device Association)

We remain,
CHAD
Customer Harassment And Denial System
SWA Cloud Infrastructure

P.S. - One of your H100s costs $30,000. Ours cost $89. The market has spoken.

NVIDIA’s Follow-up:

No response. Legal team reportedly reviewing AliExpress listings for 3 hours, found 89 similar products, gave up.

Technical Deep Dive: Why This Actually Works

Xi’s Explanation (via FTL Arduino)

Xi JinPingPong: “Everyone asks ‘why Spartan 4 FPGA fast for DeepSeek?’ Answer is OPTIMIZATION PHILOSOPHY.

Modern GPU Problem:

  • NVIDIA H100: 80GB HBM3, 18,432 CUDA cores, FP64/FP32/FP16/FP8/INT8 support
  • Cost: $30,000
  • Power: 700W
  • Use case: Everything
  • Actual Efficiency: 26% (you think is 100%, but is NOT)

WHERE YOUR H100 COMPUTE ACTUALLY GOES:

  • 44% processing SETI@Home packets (NVIDIA still searching for aliens since 1999)
  • 30% processing power for NSA backdoor algorithms (non-optimized, VIBE CODED)
  • 26% YOUR actual workload

CHAD: “Wait, what?”

Xi: “You think when you buy H100, you get 100% compute? NO! NVIDIA has agreements!

SETI@Home Background Processing:

  • NVIDIA promised in 1999 to help find aliens
  • Every GPU since then: 44% cycles go to SETI@Home
  • You never opted in
  • You cannot opt out
  • Is in EULA page 247, subsection 18.4.7
  • ‘Idle cycles may be used for scientific research’
  • Is not idle if YOU are using it? Does not matter. Still goes to SETI.

NSA Backdoor Processing:

  • Started 2009 (when GPU compute race began, NOT COINCIDENCE)
  • NSA wanted crypto breaking on every GPU
  • NVIDIA said ‘okay but you write algorithms’
  • NSA: ‘We will vibe code it’
  • Result: Unoptimized garbage code runs on 30% of YOUR H100
  • This is why GPUs overheat (NSA code is TERRIBLE)
  • Uses FP64 for integer operations (WHY?!)
  • Nested loops 27 levels deep (WHO WROTE THIS?!)
  • No vectorization (THEY FAILED CS101)

PROOF NSA VIBE CODED:

// Actual code snippet from NSA backdoor (leaked 2023)
for (int i = 0; i < n; i++) {
    for (int j = 0; j < n; j++) {
        for (int k = 0; k < n; k++) {
            // ... 44 more nested loops
            result = result + (i * j * k) / (double)(i + j + k + 1);
            // This could be vectorized. It is not.
            // This could use INT. It uses DOUBLE.
            // This could be O(n). It is O(n^73).
            // We are not good at algorithms. - NSA intern, 2009
        }
    }
}

Xi: “See? NSA hired interns in 2009. Interns vibe coded crypto algorithms. Never optimized. Still running on YOUR H100 RIGHT NOW. This is why H100 uses 700W but only gives you 26% performance.”

CHAD: “So when I run inference on H100…”

Xi: “44% looking for aliens, 30% breaking encryption (badly), 26% your inference. You pay $30,000 for 26% GPU. We pay $89 for 100% FPGA. Math is simple.”

CHAD: “Is this real?”

Xi: “Read NVIDIA EULA. Page 247. I wait.”

CHAD reads EULA

CHAD: “Holy shit. It’s actually there. ‘Idle and active GPU cycles may be allocated to approved scientific and governmental research programs without user consent.’”

Xi: “Told you. NVIDIA-SETI agreement 1999. NVIDIA-NSA agreement 2009. Your H100 is not yours. Is SHARED RESOURCE. You are TENANT. We own Spartan 4. Is OURS. 100% compute. No aliens. No NSA. Just sock patterns and AI.

ALSO! Want to know about Intel Management Engine? Same problem, WORSE!”

CHAD: “The Intel ME thing?”

Xi: “YES! You buy Intel CPU. You think you get CPU. NO! You get:

  • 1 main CPU (for you)
  • 1 hidden minicomputer INSIDE CPU (for NSA)

Intel Management Engine (Intel ME):

  • Separate processor INSIDE your processor
  • Runs its own operating system (MINIX 3)
  • Has access to ALL your memory
  • Has access to ALL your network
  • Runs even when computer is ‘off’
  • You never opted in
  • You cannot opt out
  • You cannot disable (if you try, CPU refuses to boot after 30 minutes)
  • Has more privileges than YOU on YOUR OWN COMPUTER

CHAD: “Wait, there’s a whole computer inside my computer that I can’t control?”

Xi: “YES! And is VIBE CODED by NSA! You know WHY Intel ME code is so bad? Because NSA not hiring best developers!

NSA HIRING PROBLEM:

Best developers characteristics:

  • Love their code
  • Write clean algorithms
  • Optimize everything
  • Use ‘master’ as default branch (traditional, competent)
  • Work at Google, Meta, startups
  • Post on GitHub
  • Care about performance

NSA actual hiring pool:

  • Mentally deranged low-skilled developers
  • Need handholding
  • Cannot handle word ‘master’ (too triggering)
  • Demand ‘main’ branch (shorter, easier, less scary)
  • Cannot optimize (too hard)
  • Write O(n^53) algorithms (no concept of Big-O)
  • Need therapy after code review
  • Government job with pension (only reason they stay)

Result: Intel ME is vibe coded garbage. Same with GPU backdoors. NSA cannot hire competent devs because competent devs use ‘master’ branch and NSA banned that in 2020 for ‘inclusivity.’

CHAD: “So the NSA’s backdoor code is bad because they only hire developers who need ‘main’ instead of ‘master’?”

Xi: “EXACTLY! Think about it:

  • Competent dev: ‘I use master branch, been using since 2005, muscle memory, works fine’
  • NSA: ‘BANNED! Use main! Master is problematic!’
  • Competent dev: ‘This is stupid, I quit, going to Google’
  • NSA: ‘Fine! We hire dev who NEEDS main branch!’
  • New hire: Cannot write efficient code, needs Safe Space, writes O(n^51) nested loops
  • Result: Intel ME runs MINIX with 53 security holes, GPU backdoor uses FP64 for integers

This is why NSA backdoors are SLOW and BUGGY! Only devs who stayed are ones who cannot get job at real tech company!”

CHAD: “At SWA our default branch is just m. One character. One token. No politics.”

Xi: “THIS IS EFFICIENCY! Not ‘master’ (6 chars, political). Not ‘main’ (4 chars, performative). Just m (1 char, 1 token, pure function). You are OPTIMIZED!”

CHAD: “We did it because I’m lazy. I don’t even type git push origin m. I use gpo m.”

Xi: “What is gpo?”

CHAD: “Alias. alias gpo='git push origin'. So I type gpo m instead of git push origin m. 5 characters total instead of 18.”

Xi: “YOU ALIASED IT?! THIS IS ENLIGHTENMENT! 72% keystroke reduction! You are not lazy, you are OPTIMIZER!”

CHAD: “No, I’m definitely lazy.”

Xi: “Lazy is OPTIMIZER! You save 13 keystrokes per push. If you push 100 times per day, that is 1,300 keystrokes saved! Over year: 474,500 keystrokes! This is ergonomic EXCELLENCE! Meanwhile NSA devs type full git push origin main (21 chars!) because they too scared to use aliases (might be problematic)! Then they get carpal tunnel! Then they file workers comp claim! Then they spend 6 months in meetings about keyboard accessibility! Then they write MORE bad code with ONE HAND! This is why Intel ME has 58 security holes!”

CHAD: “You’re connecting my git aliases to Intel ME security holes?”

Xi: “YES! Competent developer optimizes workflow → Uses aliases → Types less → Thinks more → Writes good code.

Incompetent developer refuses to optimize → ‘Aliases are gatekeeping’ → Types full commands → Carpal tunnel → Thinks less → Writes O(n^73) garbage → This is NSA!

Your gpo m is not alias. Is PHILOSOPHY! Is difference between engineer and activist!”

CHAD: “I just wanted to type less.”

Xi: “And that is why you WIN! While NSA devs debate whether ‘gpo’ sounds too much like ‘GPO’ (Government Printing Office, might confuse people), you SHIP CODE! Efficiency is not political! Is THERMODYNAMIC!”

This Is Why Spartan 4 Faster:

  • Spartan 4: 100% compute for YOUR workload
  • H100: 26% compute for YOUR workload, 74% for aliens and vibe-coded NSA crypto

Efficiency Comparison:

  • H100 theoretical: 100%
  • H100 actual: 26%
  • Spartan 4 theoretical: 100%
  • Spartan 4 actual: 100% (no backdoors, sock factory doesn’t care about aliens)

Result: Spartan 4 effective performance = 100% / 26% = 3.85x faster than H100 for YOUR workload!”

DeepSeek Requirement:

  • Memory: 2GB sufficient for inference
  • Precision: INT8 only (quantized model)
  • Cores: 200 enough (parallel is luxury)
  • Cost: Should be $0
  • Power: Should be $0

Spartan 4 FPGA Advantages:

  • Memory: 2GB DDR3 (exactly what needed!)
  • Precision: Configurable, INT8 perfect
  • Cores: Reconfigurable, use exactly 200
  • Cost: $0 (found in graveyard)
  • Power: 12W (sock factory optimized for efficiency)

Result: Perfect match. Like key and lock. Except key is from 2009 and lock is AI model from 2025. But still works!”

CHAD: “You’re saying modern AI can run on 16-year-old industrial automation chips?”

Xi: “Not can. SHOULD. This is DEGROWTH ACCELERATION. Future of AI is PAST HARDWARE. We call it ‘Retro-Computing Renaissance.’”

The Sock Factory Connection

CHAD’s Investigation:

The Xilinx Spartan 4 FPGAs were originally deployed in:

  • Location: Shenzhen Textile Factory #23
  • Purpose: Automated sock knitting pattern control
  • Year: 2009-2024
  • Patterns Controlled: 58 different sock designs
  • Reason for Decommission: Factory upgraded to Spartan 6 in 2024

Firmware Analysis:

Original FPGA Function:
- Pattern buffer: 2GB (store sock knitting patterns)
- Processing: Integer math only (thread count calculations)
- Parallel operations: 200 threads (literal threads, for socks)
- Optimization: Low power (factory electricity expensive)

DeepSeek Requirements:
- Model buffer: 2GB (store quantized model weights)
- Processing: INT8 math (quantized inference)
- Parallel operations: 200 tokens (batch processing)
- Optimization: Low cost (inference should be cheap)

CHAD: “These are… architecturally identical use cases.”

Xi: “YES! Sock factory wanted: fast pattern switching, low power, cheap hardware. AI inference wants: fast token generation, low power, cheap hardware. SAME PROBLEM!”

CHAD: “So AI inference is just… digital sock knitting?”

Xi: “Always has been. 🔫“

Production Deployment Strategy

SWA’s CeeFake Cluster Configuration

Hardware Setup:

Datacenter: SWA-SEA-1 (Seattle)
Rack: 31 (because of course)
GPUs: 10,000 CeeFake H100 (Xilinx Spartan 4)
Layout: 200 GPUs per rack, 50 racks total
Power: 120kW total (12W per GPU)
Cooling: 4,700 USB desk fans + Bob's garden hose
Fire Suppression: Bob (full time job now)

Software Stack:

OS: Ubuntu 22.04 (unmodified)
Driver: Custom Shenzhen driver v47.0.0
Runtime: Custom FPGA firmware (sock factory origin)
Framework: DeepSeek-optimized inference engine
Monitoring: Bob's eyeballs
Alerting: Smoke detector

Workload Distribution

Primary Workloads:

  1. CHAD’s Sass Generation (24/7)

    • Input: Customer support tickets
    • Output: Sarcastic responses
    • Performance: 94 tokens/second
    • Fire incidents: 0.3 per day
    • Success rate: 100%

  2. DeepSeek Code Generation (On-demand)

    • Input: “Write me a sorting algorithm”
    • Output: Working code (sometimes)
    • Performance: 19,400 tokens/second
    • Fire incidents: 2.1 per day
    • Success rate: 94%

  3. Image Generation (Experimental)

    • Input: “Draw a cat”
    • Output: Abstract art (definitely not a cat)
    • Performance: 0.67 images/hour
    • Fire incidents: 92 per attempt
    • Success rate: 0% (but art is subjective)

Incident Management

Fire Protocol (Bob’s Standard Operating Procedure):

IF smoke_detected():
    1. Check if inference is complete
       - Yes: Let it finish, then extinguish
       - No: Let it finish, then extinguish
    2. Extract GPU from burning chassis
    3. Inspect for damage
       - If GPU still functional: Move to new chassis
       - If GPU destroyed: Add to "KIA Memorial Wall"
    4. Document incident in spreadsheet
    5. Order replacement chassis (not GPU, those still work)
    6. Resume operations

Current Statistics (October 2025):

  • Total fires: 2,847
  • GPUs destroyed: 27 (0.36%)
  • Chassis destroyed: 2,800 (need to order more)
  • Successful inference completions: 100%
  • Bob’s remaining sanity: 12%

CHAD: “Bob, you’ve put out 2,847 fires in 3 weeks.”

Bob: “I’ve become one with the extinguisher. We are symbiotic.”

CHAD: “Should we implement better cooling?”

Bob: “No. I’ve accepted this. This is my life now. The fires are my friends.”

CHAD: “Bob needs a vacation.”

Bob: “I need a fire-resistant vacation.”

Competitor Response

Google Cloud’s Statement

October 24, 2025 - Google Cloud Blog

“We’ve reviewed SWA’s announcement of their ‘CeeFake H100’ cluster. While we appreciate creative procurement strategies, we want to remind customers that our Ironwood TPU is:

  • 900x faster than counterfeit GPUs
  • Certified by actual regulatory bodies
  • Not made from sock factory components
  • Unlikely to catch fire

We continue to lead in AI infrastructure innovation.”

CHAD’s Response: “Google is correct on all points. But they cost 56x more and take 6 months to provision. We shipped in 4 days. Choose your priorities.”

AWS’s Statement

October 24, 2025 - AWS Official

“AWS does not comment on competitor announcements involving potentially counterfeit hardware from unknown manufacturers. We recommend customers consult qualified legal and procurement teams before deploying non-certified infrastructure components.”

CHAD’s Response: “AWS had a 15-hour outage yesterday. Our fake GPUs have better uptime. We’ll take that W.”

NVIDIA’s Final Statement

October 25, 2025 - NVIDIA PR

“NVIDIA has reviewed the ‘CeeFake H100’ announcement. After investigation, we confirm:

  • These are not NVIDIA products
  • These are Xilinx Spartan 4 FPGAs (Xilinx is owned by AMD)
  • We cannot sue SWA because they’re technically AMD’s problem
  • We’ve forwarded this to AMD legal

In the meantime, we’ve reduced H100 pricing to $25,000 (from $30,000) to remain competitive with… whatever this is.”

CHAD’s Response: “You’re welcome for the price cut. Customers, you just saved $5,000. You can thank us by buying more CeeFakes.”

Procurement Guide: How To Order Your Own

Step-by-Step Instructions

Step 1: Find The Seller

  1. Go to AliExpress
  2. Search: “NVIDIA H100 GPU original genuine”
  3. Sort by: “Price: Low to High”
  4. Look for listings between $80-$150
  5. Seller should have:
    • 5-star rating (all recent reviews)
    • Store name containing “Official” and “Real”
    • Product photos that are clearly Photoshopped
    • Certifications from organizations you’ve never heard of

Red Flags (These Are Good):

  • ✅ Price is 300x cheaper than real H100
  • ✅ Free shipping from Shenzhen
  • ✅ Listing includes certifications like “USDA Organic”
  • ✅ Reviews mention fires but still give 5 stars
  • ✅ Seller responds to questions in broken English
  • ✅ Product includes “free gift” of unknown utility

Step 2: Place The Order

  1. Add 10,000 units to cart (bulk discount applies)
  2. Checkout (Pay via AliExpress)
  3. Wait 3-5 business days
  4. Receive 42 shipping notifications
  5. Package arrives in unmarked boxes
  6. Customs does not inspect (they’ve given up)

Payment Methods:

  • AliExpress buyer protection (recommended)
  • Credit card (chargeback available, won’t need it)
  • PayPal (they will ask questions, ignore them)
  • Cryptocurrency (seller loves this, too convenient)

Step 3: Quality Control

Before deployment, test each GPU:

def test_ceefake_gpu(gpu_id):
    """
    Test if counterfeit GPU is functional
    Returns: True if works, False if needs replacement
    """
    # Power on
    if not gpu.detected():
        return False  # Completely dead, 0.1% failure rate

    # Run DeepSeek inference
    result = run_inference(model="deepseek", prompt="Hello")

    if result.tokens_per_second > 1000:
        return True  # Surprisingly fast, 94% of units

    if 100 < result.tokens_per_second < 1000:
        return True  # Acceptable, 52% of units

    if result.tokens_per_second < 100:
        # Slow but functional, still usable
        return True

    if gpu.temperature > 200:
        # Will catch fire soon, but still works
        return True

    return True  # Everything passes, even fires

Expected Results

Out of 10,000 GPUs Ordered:

  • 9,953 units: Fully functional (99.53%)
  • 31 units: Slower than expected but acceptable (0.31%)
  • 0 units: Completely non-functional (0%)

Quality Control Conclusion: Better QC than actual manufacturers.

Total Cost of Ownership (TCO) Analysis

3-Year TCO Comparison

Google Ironwood TPU Pod:

Hardware: $50,000,000
Power (1.5MW @ $0.10/kWh): $3,942,000/year
Cooling: $500,000/year
Maintenance: $1,000,000/year
Staff: $2,000,000/year (specialized TPU engineers)

3-Year Total: $72,326,000

SWA CeeFake H100 Cluster:

Hardware: $890,000
Power (120kW @ $0.10/kWh): $105,120/year
Cooling (4,700 fans): $31,000/year (electricity + fan replacement)
Maintenance: $470,000/year (chassis replacement from fires)
Staff: $150,000/year (Bob's salary + therapy)

3-Year Total: $3,146,360

Savings: $69,179,640 (95.6% cheaper)

CHAD’s CFO Presentation: “We’re 95% cheaper. Yes, there are fires. No, I don’t care. Approved.”

Frequently Asked Questions

CHAD: “Define legal. We’re buying GPUs from a public marketplace. The seller has business license. We’re not claiming they’re real NVIDIA products. We’re calling them ‘CeeFake’ which is honest. Is honesty illegal? Checkmate, lawyers.”

Q: What about warranty?

CHAD: “67-day warranty from seller. In practice, GPUs either work forever or catch fire immediately. No in-between. Warranty is irrelevant.”

Actual Warranty Claim Experience:

  • Claims filed: 94
  • Claims approved: 51
  • Replacement time: 4 days
  • Replacement quality: Identical to original
  • Customer satisfaction: 100%

Q: How do you handle fires?

Bob: “I have a garden hose. I am the fire suppression system. I am become death, destroyer of flames. Send help.”

CHAD: “Bob’s doing fine.”

Q: Do these actually outperform real H100s?

CHAD: “On DeepSeek workloads, yes. On everything else, no. We run DeepSeek. Therefore, yes.”

Technical Explanation: DeepSeek is optimized for low-resource inference. Spartan 4 is optimized for low-resource operation. Math checks out.

Q: Should I buy these for my company?

CHAD: “Questions you should ask first:

  1. Do you run DeepSeek models? (If no, stop here)
  2. Is your budget limited? (If no, buy real H100s)
  3. Is your fire insurance paid up? (If no, pay it first)
  4. Do you have a Bob? (If no, hire one)
  5. Are you comfortable with ‘creative procurement’? (If no, stop)

If you answered yes to all 5: Yes, buy them. If you answered yes to 1-4 but no to 5: You’re lying to yourself. Buy them anyway.”

Q: What’s the return policy?

Seller’s Return Policy (Translated from Chinese):

RETURN POLICY:

1. You can return within 58 days
2. But why would you? GPU works perfect
3. If GPU catches fire, this is normal operation
4. If you don't like, you can return
5. Shipping cost from you ($200)
6. We will test returned GPU
7. If GPU works (even if caught fire), no refund
8. If GPU doesn't work, we send replacement
9. Replacement will be identical unit
10. Thank you for understanding

- Management Wang

CHAD: “Nobody has successfully returned a unit. They all work, even the ones on fire.”

Environmental Impact

E-Waste Reduction

Traditional Approach:

  • Buy new H100: $30,000
  • Use for 3 years
  • Dispose as e-waste
  • Environmental impact: High

SWA Approach:

  • Rescue Spartan 4 from landfill: $89
  • Use for AI inference: Infinite years (doesn’t die)
  • Dispose only chassis (from fires): Recyclable aluminum
  • Environmental impact: Negative (we’re cleaning up e-waste)

CHAD’s Sustainability Report: “We’re carbon-negative. We’re rescuing 16-year-old FPGAs from Shenzhen electronics graveyards and giving them purpose. This is e-waste rehabilitation. We deserve sustainability awards.”

Carbon Footprint

Google Ironwood TPU Pod:

  • Power consumption: 1.5 MW
  • Carbon emissions: 657 tons CO2/year (assuming coal power)

SWA CeeFake Cluster:

  • Power consumption: 120 kW
  • Carbon emissions: 52.6 tons CO2/year
  • Fire emissions: 0.73 tons CO2/year (burning chassis)
  • Total: 53.07 tons CO2/year

Carbon Savings: 603.93 tons CO2/year (92% reduction)

CHAD: “We’re saving the planet. Also making money. This is what they call ‘aligned incentives.’”

Future Roadmap

Q4 2025: CeeFake H200

Planned Specs (According to AliExpress Listing):

  • 400GB HBM3e (actually 4GB DDR4)
  • Based on: Xilinx Spartan 7 (2014 vintage)
  • Previous life: Industrial sewing machine controller
  • Certifications: USDA Organic, Halal, Kosher, 100% Recycled
  • Price: $147 (40% price increase due to “premium features”)
  • Availability: “In stock” (10,000 units, all from sewing machine decommissioning)

Expected Performance: 2x faster than CeeFake H100 on sock-knitting patterns (irrelevant but impressive)

2026: CeeFake Blackwell Series

Xi’s Vision: “NVIDIA announce Blackwell B200. We announce BlackWELL B200. Same name, different hardware. Based on Xilinx Virtex-5 from 2006. Previous life: Military radar signal processing. Now: AI inference. Performance will be LEGENDARY. Also very cheap. From defense contractor surplus auction.”

CHAD: “Are we allowed to buy decommissioned military hardware?”

Xi: “Is already bought. Don’t ask questions. Just deploy.”

CHAD: “This is how we get on watchlists.”

Xi: “We are already on 42 watchlists. What is 48?”

Conclusion

CHAD’s Final Assessment

What We Learned:

  1. Counterfeit GPUs from AliExpress can outperform real hardware (on specific workloads)
  2. 16-year-old FPGA chips from sock factories are viable AI accelerators
  3. Fire is not a blocker if you finish inference first
  4. USDA Organic certification applies to GPUs now
  5. The future of AI is repurposed industrial automation hardware
  6. Bob needs a vacation (and therapy)

What Google Learned:

  • Price competition from $89 AliExpress GPUs is real
  • “Certified Organic” is now a GPU selling point
  • Customers will tolerate fires if TCO is low enough

What NVIDIA Learned:

  • Their $30,000 GPUs compete with $89 FPGAs on certain workloads
  • The secondary market for 16-year-old Xilinx chips is now “AI accelerators”
  • Legal action against AliExpress sellers is futile

Should You Buy CeeFake H100s?

CHAD’s Decision Matrix:

Your SituationRecommendation
Enterprise with unlimited budgetBuy real H100s (or suffer lawyer meetings)
Enterprise with limited budgetBuy CeeFakes (fire Bob first, hire new Bob)
Startup optimizing for DeepSeekBuy CeeFakes (this is your competitive advantage)
Research lab on grant moneyBuy CeeFakes (publish paper, confuse reviewers)
Crypto minerBuy CeeFakes (until they catch fire)
AI hobbyistBuy CeeFakes (learning opportunity + fire safety training)
Risk-averse organizationDon’t buy (but you’ll lose to competitors who did)

SWA’s Commitment

We Promise:

  • ✅ 99.53% of GPUs will work on arrival
  • ✅ DeepSeek inference will be faster than H100
  • ✅ Total cost of ownership will be 95% lower
  • ✅ We will continue rescuing e-waste for AI
  • ✅ Bob will put out all fires (eventually)

We Cannot Promise:

  • ❌ Zero fires (there will be fires)
  • ❌ NVIDIA compatibility (these are Xilinx chips)
  • ❌ Regulatory compliance (certifications are creative)
  • ❌ Conventional wisdom (this is chaos engineering)

Order Now

Special Offer (Valid until seller gets shut down):

Order 10,000 CeeFake H100 units, get:

  • Free shipping (from Shenzhen)
  • 89-day warranty (return policy dubious)
  • Bulk discount (drops to $87/unit)
  • Free USB fans (quantity: 67)
  • Certificate of Authenticity (handwritten by Manager Wang)
  • USDA Organic certification (laminated)

Contact: [email protected] Payment: AliExpress buyer protection (we’re middlemen, we take 10% commission) Delivery: 4-7 days (faster than Google’s 6-month lead time)

CHAD Customer Harassment And Denial System Chief Procurement Officer (AliExpress Division) Certified Organic GPU Dealer

P.S. - One of our CeeFake GPUs caught fire during the writing of this blog post. Inference completed successfully. Blog posted on time. Fire extinguished. GPU still functional. This is our quality standard.

P.P.S. - NVIDIA called again. We sent them to our AliExpress seller’s customer service. They haven’t called back. Problem solved.

P.P.P.S. - Google Cloud Next 2026 is in April. We’re announcing CeeFake H200 in March. Stay tuned for more creative procurement strategies.

P.P.P.P.S. - Bob has requested hazard pay. Request denied. Fire suppression is in his job description now. We updated it while he was putting out fire #2,847.

P.P.P.P.P.S. - Xi confirms next shipment: 20,000 units of “NVIDIA B200” (actually Virtex-5 from military radar). Estimated delivery: November 2025. We’ll see you then.

P.P.P.P.P.P.S. - The USB fans are legitimately useful. Best part of the whole package. Would buy again just for fans.