I’m in the middle of building my second GPU cluster, and I’m losing hair (literally) staring at current RAM, SSD and GPU prices. They are not what they were 18 months ago.
In Part 1 of this series I walked through the V1 build — a dual RTX 3090, 48 GB VRAM rig that came together over six weeks at the end of 2024. This post is a different angle on the same machine. I kept every receipt. In April 2026 I went back and looked up what the same parts cost today, from the same retailers where I could. The result is a line-item view of what 18 months of the AI buildout did to the parts I bought.
I have skin in the game. Most AI Bubble takes don’t have skin in the game. They are macro analysis from people watching stock prices, supply crunches and second-order effects from a distance. I live this; this is my world, and I deal with the ramifications every day. This post is the opposite: receipts first, mechanisms second, opinions third.
The Receipts
Full Comparison Table
| Part | 2024 Paid | 2026 Sticker | Δ % | 2026 Source |
|---|---|---|---|---|
| RTX 3090 Founders Edition | $629.96 | $1,050.00* | +66.7% | Best Value GPU |
| RTX 3090 Founders Edition | $699.99 | $1,050.00* | +50.0% | Best Value GPU |
| Ryzen 9 7950X3D | $579.99 | $699.99 | +20.7% | Micro Center |
| ASUS ProArt X870E-Creator | $479.99 | $539.99 | +12.5% | Micro Center |
| TeamGroup T-Create Expert 96GB DDR5-6800 | $345.99 | $1,299.99 | +275.7% | Micro Center |
| Samsung 990 PRO 2TB NVMe | $144.99 | $639.99 | +341.4% | Micro Center |
| Lian Li Galahad II LCD | $289.99 | $284.99 | -1.7% | Micro Center |
| Corsair AX1600i | $609.99 | $609.99 | 0.0% | Corsair.com |
| Lian Li O11 Dynamic EVO XL | $234.99 | $289.99 | +23.4% | Micro Center |
| Lian Li UNI Fan SL-Infinity 3-pack | $89.99 | $79.99 | -11.1% | Micro Center |
| Lian Li Fan Reverse SL-Infinity ×4 | $119.96 | $123.96 | +3.3% | Micro Center |
| Corsair 12-pin GPU power cable | $29.98 | $19.99 | -33.3% | Corsair.com |
| Corsair 12-pin GPU power cable | $26.98 | $19.99 | -25.9% | Corsair.com |
| Total | $4,282.79 | $6,708.86 | +56.6% |
| Totals — hardware only, pre-tax | |
|---|---|
| 2024 paid | $4,282.79 |
| 2026 sticker (April 22, 2026) | $6,708.86 |
| 2026 sale (where active discounts applied) | $6,432.86 |
| Δ at sticker (18 months) | +$2,426.07 / +56.6% |
If I walked into Micro Center tomorrow with my V1 parts list, I’d pay about $2,400 more than I did. That’s the headline. But the headline understates what’s actually happening, because the average is hiding a divergence. What was once a GPU bubble has now expanded to memory, storage, and most recently CPUs, while some parts remain untouched (for now).
The Divergence
Most of the build didn’t move. Four components did. Massively.
Down or Flat:
- cooler, fans (3 pack), GPU power cables: down in price
- PSU: flat at $609.99
Modest moves:
- case, motherboard, fans (4 pack): up modestly (3%-23%)
Big Moves
- RAM: +275.7%
- Storage: +341.4%
- GPUs (used market): +50.0% to +66.7%
- CPU: +20.7%
Four product categories out of thirteen components account for almost all of the price movement. They aren’t random. They’re the parts where consumer demand competes directly with AI infrastructure for the same supply pool — DRAM fab capacity, NAND wafer output, last-gen used GPUs and consumer CPU fab capacity being reallocated to server class chips. The other eight parts in this build moved in line with general consumer PC pricing. (To be clear: AI infrastructure consumes plenty of cooling, interconnect, networking, and power too — but those go through different product categories than what’s on a Micro Center shelf.)
That’s the chart-stopping observation: the AI buildout isn’t lifting all hardware. It’s specifically revaluing the consumer-PC components that share supply chains with AI infrastructure. Four numbers. Four mechanisms.
Four Mechanisms
1. RAM (+275.7%): HBM Allocation Crowded Out DDR5 Capacity
I paid $345.99 for a 96 GB DDR5-6800 kit (TeamGroup T-Create Expert) in November 2024. The same kit today: $1,299.99. The mechanism isn’t that DDR5 itself became four times more useful. It’s that the manufacturers making it reallocated capacity to a higher-margin product: HBM.
HBM — High Bandwidth Memory — is what Nvidia H100s, B200s, and AMD MI300s use. It’s manufactured from the same DRAM die fabs that produce DDR5, but uses through-silicon-via stacking and advanced packaging. When AI accelerator demand spikes, manufacturers shift wafer and packaging capacity from DDR5 to HBM, because HBM commands several times the margin per wafer. DDR5 supply tightens. DDR5 prices rise. Ultimately, if you have a large scale buyer ready to sign a multi-year deal for a significant share of your DRAM output, you don’t say no. That’s the big picture; large institutions are inking multi-year commitments because the alternative is being priced out later. If you don’t lock in memory now, your competitor will – and you’ll be paying a premium for the same chips next year.
In October 2025, OpenAI signed a letter of intent with Samsung and SK Hynix to supply memory chips for the Stargate project — up to 900,000 DRAM wafers per month, which industry analysts estimated could consume up to 40% of global DRAM output. The deal covers HBM, GDDR, high-capacity SSDs, and LPDDR5X-PIM — essentially the entire memory stack relevant to AI inference and training. Within months, DDR5 ECC RDIMM prices started climbing. The Kingston Fury Renegade Pro kit that Micro Center quoted me at $1,999 in early 2026 is now quoted at $4,500 (pre-tax) for the same SKU. The DDR5 wave on consumer and prosumer kits is the downstream wake of that capacity reallocation.
2. Storage (+341.4%): NAND Reallocation Toward Enterprise
Fun fact: at the time of writing this post, a $25,000 USD investment into SanDisk ($SNDK) one year ago would be worth roughly $1 million USD today. If you’re thinking, “is SanDisk a memory company?” – yes. They IPO’d as a Western Digital spinoff in February 2025 and are up about 37x in the past year on AI memory demand. The downstream effect on what builders pay is just as visible. I paid $144.99 for a Samsung 990 PRO Gen 4 2TB in November 2024. Same drive today: $639.99. Same retailer – a 341% increase.
AI does not just eat GPUs. It eats storage too. Training datasets, model checkpoints, vector databases for retrieval, and KV-cache spillover when contexts exceed GPU memory all need fast NVMe storage. As hyperscalers ramped AI infrastructure through 2025 and 2026, manufacturers had every reason to prioritize enterprise SSDs: bigger contracts, better margins, and multi-year demand commitments from cloud and data-center customers.
That left consumer and prosumer drives in a rough spot. A drive like the Samsung 990 PRO was not just being priced against normal PC-builder demand anymore. It was part of a much larger NAND supply fight, where AI data centers were pulling the market toward enterprise storage. The result was exactly what PC builders felt at checkout: tighter supply and higher prices.
The pressure isn’t limited to NVMe. In February 2026, Western Digital’s CEO confirmed the company is sold out of hard drive production for all of 2026, with long-term supply agreements stretching into 2027 and 2028. Seagate’s nearline capacity is similarly allocated. That’s how badly storage has been hit by the AI buildout.
3. GPUs (+50.0% to +66.7%): The Used-Market Obliteration
GPUs are a different story. The RTX 3090 FE was discontinued a long time ago. There is no 2026 retail sticker. The closest comparable is the eBay used market, tracked by Best Value GPU.
I paid $629.96 and $699.99 for my two 3090 FEs. Today’s used median: $1,050. That’s a 50-67% return on both GPUs.
The mechanism here runs through the new-card stack. The RTX 5090 launched at premium pricing. Scalpers compressed the new-card market further. Buyers who couldn’t get 5090s — or wouldn’t pay scalped prices — rolled down-stack to last-gen used. The 3090’s 24 GB VRAM kept it relevant for local LLM work specifically, which kept demand pressure on the used market. Last-gen flagship VRAM became a scarce commodity through demand-side substitution from above.
In this case, the RTX 3090 shone like a bright light in the darkness of insane prices. The 4090 (also discontinued) is technically the better card for local AI. It has the same 24 GB of VRAM as the 3090, more tensor cores, and Ada Lovelace’s stronger kernel performance for modern inference paths. But cost-per-compute is what wins when you’re spending your own money, not a hyperscaler’s. At roughly $1,050 used versus $2,000-$2,500+ for a 4090, you are paying nearly double for the same VRAM ceiling.
Through inflation, the AI bubble compounding monthly, and everything getting more expensive across the board, the 3090 continues to reign king for cost-conscious local AI.
4. CPUs (+20.7%): Server-CPU Pressure from Agentic AI
As Jensen, Altman, Ellison and Zuckerberg sat around a circular table and decided who would be the next victim of the AI Bubble – they landed on CPUs. I paid $579.99 for a Ryzen 9 7950X3D in November 2024. Same chip today: $699.99 – a 20.7% bump. It’s also a warning of what’s to come.
Consumer desktop CPUs are NOT where the AI buildout is hitting hardest. Server CPUs are. Agentic AI workloads — the kind that spin up sandboxes, run tests, execute scripts, compile projects — are CPU-bound, not GPU-bound. Every Claude Code or Codex agent run consumes server-CPU cycles at scale. GitHub COO Kyle Daigle reported in April 2026 that platform commit volume hit 275 million per week (up from ~1 billion total in 2025) and GitHub Actions runner consumption jumped from 500M minutes/week in 2023 to 2.1 billion minutes in a single week in 2026. His comment: “we’re pushing incredibly hard on more CPUs.”
Intel said in its Q1 2026 earnings call that the CPU-to-GPU deployment ratio in datacenters is tightening from 1:8 toward 1:1 in agentic scenarios. Server CPU lead times have stretched from 1–2 weeks to six months at Intel and 8–12 weeks at AMD. To keep up, Intel is reallocating consumer chip production to Xeon. AMD has pushed Ryzen prices up 10% in a single month with another 8–10% hike expected in H2 2026. Cumulative consumer CPU pricing is forecast to rise 16–17% through 2026.
The mechanism is the same shape as RAM and Storage: enterprise demand pulls consumer supply. My 20.7% Ryzen bump is the leading edge. The full wave is still coming. It’s clear that, at the time of writing this, CPUs are likely to be the next victim of the AI Bubble.
The Outliers
It’s worth calling out the components that didn’t move, because they tell you what’s not part of the story.
- PSU: $609.99 then, $609.99 now. Flat. PSUs don’t bottleneck AI workloads – energy does.
- CPU Cooler: -1.7%
- GPU power cables: -25% to -33%. Niche accessory, demand softened
- Fans: -11.1%
If your build only needed PSU, cooler, fans, and cables, you’d actually save money in 2026. From real-world experience, the AI bubble’s consumption pattern is clear right now, as mechanism four makes its case. CPUs are no longer ‘next’, they are already here.
The Fine Print
Three things to note before anyone tries to make this into a macro thesis:
1. n = 1: This is one builder’s parts list, not a market index. I bought from specific retailers (Micro Center, Corsair direct, Amazon for the spare fan). Other regions and retailers may differ.
2. Sticker vs paid: My 2024 column is what I actually paid (pre-tax). The 2026 sticker column is non-sale retail at one point in time (April 22, 2026). The 2026 sale column shows what was available with active discounts. The “real” 2026 cost depends on patience and timing — see the sale total ($6,432.86) for the disciplined-shopper version.
3. Used GPU column is not retail: The RTX 3090 FE is discontinued. The $1,050 figure is eBay used median from Best Value GPU’s tracker. There is no equivalent retail comparison.
These caveats matter because the moment you say “AI bubble” out loud, legend has it a boomer will manifest and start talking about how life was better before AI.
What This Means For the Next Builder
If you’re planning a local AI rig in mid-2026, a few observations from this data:
1. Don’t wait on RAM: DDR5 prices are still moving. Every month I delayed my V2 RAM purchase, it cost me real money. The supply story behind these prices doesn’t unwind quickly.
2. The used GPU market is the entry point: New-card retail is broken at the high end. eBay, r/hardwareswap, Best Value GPU’s tracker — that’s the rational path for last-gen flagship VRAM. Build your budget around used. The RTX 50 series are priced crazy – all GPUs are priced crazy. As we enter the half way month of 2026 – data centers, hyperscalers, boutique scalers, are going to sell older cards, such as the A100s. If you are single, and don’t have a wife – an A100 could be the move, but they are VERY loud and require experience to retrofit for a home local set up.
3. Commodity parts are still safe to time: Case, PSU, cooler, fans, cables — these didn’t move and aren’t likely to (for now). You can wait on those without penalty if cash flow matters.
4. Buy your CPU now if you need one: Mechanism 4 covered why server-side CPU pressure is pulling consumer pricing. AMD has already hiked Ryzen 10% in the last month alone, with another 8-10% expected in the near future. The leading edge is moving. Don’t be the one paying the second hike.
The cost of patience isn’t symmetric. On AI-consumed parts, waiting is expensive. On commodity parts, waiting is free. Time your spending accordingly.
What’s Next
Part 3 of this series is the research running on the rig — agentic coding benchmarks, model comparisons, and what 48 GB of VRAM actually buys you. Less hardware, more inference, coming soon to this blog series.
The wider context behind these price mechanisms — Aschenbrenner’s 100 GW cluster projections, Huawei’s domestic AI silicon push and DeepSeek, US export controls of Nvidia chips to China, Amodei’s data-center-of-geniuses framing and the circle jerk of large hyperscalers rotating in giving each other the same amount of money — is its own essay, and one I’ll likely write separately. What this data tells me is narrower but concrete: the AI buildout is consuming hardware in ways that have visible, line-item consequences for individual builders. If you’re not at a hyperscaler, you’re fighting for the scraps of whatever is left. And that sucks.
*This is part of Project Arrival — my journey from Senior Data Scientist to AI research engineer. I’m documenting the build, the research, and the path to joining a top AI lab. Follow along at blog.zacharycangemi.com or at my GitHub https://github.com/zacangemi