On the future of hardware pricing
Disclaimer: Analysis only, not financial advice. Sources: Micron whitepapers, NVIDIA GTC 2026 keynote, Futurum Group, SemiAnalysis InferenceX, Jensen Huang's 1GW iso-power slide. All projections are inference from cited data.
Hardware prices are being distorted by AI demand, and it won't resolve until at least 2027. Here's why — and what it means for hosts and powerusers.
The prisoner's dilemma
Hyperscaler capex/revenue sits at 10:1 to 15:1. $500B committed in 2026 alone against ~$50B industry revenue [1], with an additional $500B+ committed through 2027 [2] [3]. Nobody stops building because stopping means losing market share. Sunk cost does the rest.
How inference actually works
GPU inference runs in two phases with fundamentally different computational profiles:
- Prefill — all input tokens processed simultaneously. Massively parallel matrix multiplication. High GPU utilization. This is what GPUs were built for.
- Decode — output tokens generated one at a time, sequentially. Each token depends on the previous one — parallelization is architecturally impossible. GPU sits mostly idle between steps, waiting on memory reads.
Four solutions have been deployed to attack this:
- Batching — sharing GPU compute across concurrent users, amortizing idle decode time
- HBM capacity scaling — keeping the KV cache (the running memory of the conversation) close to compute, reducing fetch latency
- SOCAMM LPDRAM [4] — CPU-attached memory tier up to 2TB per CPU, staging warm KV cache outside expensive HBM
- LPU (Groq) [5] — dedicated decode hardware with 500MB on-chip SRAM per chip, statically compiled execution graph, zero scheduling overhead. Does one thing: generates tokens fast.
The result is a structural collapse in cost per million tokens — not software optimization, hardware architecture:
| System | Year | Tokens/sec/GW | Cost/Mtoken | vs H100 |
|---|---|---|---|---|
| H100 NVL8 | 2022 | ~2M | $4.40 | 1x |
| H200 NVL8 | 2024 | ~2.8M | ~$3.00 | ~1.4x |
| GB300 NVL72 | 2026 | ~70M | $0.13 | ~35x |
| Vera Rubin NVL72 | H2 2026 | ~700M | ~$0.013* | ~350x |
| VR NVL72 + Groq LPX | H2 2026 | ~24.5B | ~$0.00037* | ~12,250x |
| Feynman/Kyber (VRU) | 2028 | TBD | TBD | ~50,000x+** |
*Approximated: VR = 1/10th GB300 per NVIDIA [6]. VR+Groq = 35x more tokens/watt vs Blackwell at ~2x combined rack cost [5].
At iso-power (1GW): 600K Hopper GPUs produce 2M tokens/sec. 300K Vera Rubin GPUs produce 700M tokens/sec using half the hardware [5]. The floor has not been reached.
**Feynman confirmed for 2028 with new GPU, Rosa CPU, LP40 LPU, and Kyber NVL1152 (8x density of Rubin NVL144) [7]. Performance trajectory is author's projection from generational improvement pattern, not NVIDIA's stated figure.
For hosting operators: you've seen this before
The traditional hosting industry prices on core count and memory density. Core pricing is being dramatically challenged. There is no better analog than this:
| CPU generation | Year | Cores (2S) | Revenue/RU vs baseline |
|---|---|---|---|
| Ivy Bridge-EP | 2012 | 20 | 1x |
| Haswell-EP | 2014 | 36 | ~1.8x |
| EPYC Naples | 2017 | 64 | ~3.2x |
| EPYC Rome | 2019 | 128 | ~6.4x |
| EPYC Genoa | 2022 | 192 | ~9.6x |
| EPYC Turin | 2024 | 256 | ~12.8x |
Same rack unit. Same colocation rent. The 2012 server still worked in 2019 — it was just priced out of existence by a neighbor in the same rack doing 6x the work at the same footprint cost. GPU token economics follow the same curve, compressed into 24 months instead of 12 years.
The idle capacity problem
$500B in 2026 AI-directed capex [1] at ~20% inference allocation implies approximately 2 trillion tokens/sec of new inference capacity — derived from Jensen's own 1GW iso-power comparison: 300K Vera Rubin GPUs at 700M tokens/sec per GW [5], blended across the Blackwell-dominant 2026 install base.
Current industry demand is approximately 127M tokens/sec — derived from ~$40B in 2026 AI revenue [8] divided by a blended ~$10/Mtoken average across consumer and enterprise tiers ($10/Mtoken × 127M tok/s × 3.15×10⁷ s/yr ≈ $40B). The gap is roughly 15,000x oversupply before Vera Rubin ships.
Even under aggressive Jevons Paradox assumptions — cheaper tokens drive proportionally more usage — demand growing 100x still leaves 150x excess capacity. The token price floor is arithmetic, not speculation:
Price floor ≈ electricity cost ÷ tokens per kWh
At VR+Groq efficiency and $0.05/kWh, the floor approaches ~$0.00037/Mtoken (consistent with table above). Current GB300 pricing of $0.13/Mtoken is already ~350x above that floor.
What this means for hardware pricing at Q4 2027 / Q1 2028
Market rationalization here means a specific trigger: hyperscalers stop or significantly reduce new orders, either from token supply glut making additional capacity economically indefensible, or from liquidity pressure as medium-term corporate bond markets tighten against sustained negative ROI. Hyperscalers raised $121B in new debt in 2025 alone [9], with Morgan Stanley and JP Morgan projecting $1.5T in total debt issuance required over the next few years [10]. Oracle already faces a financing gap from FY2027, with Barclays warning it could run out of cash by November 2026 at current trajectory [11]. CDS spreads — the bond market's forward-looking default insurance — have been rising across the sector [12].
This aligns roughly with the end of NVIDIA's currently committed production pipeline. If either condition materializes earlier — and the oversupply math suggests it could — the price corrections described below arrive ahead of this timeline, potentially as early as mid-2027.
Compute (GPU)
The 2026 installed Blackwell base faces a competitive token economics gap of 350x against Vera Rubin alone, 12,250x against VR+Groq.
Hourly GPU rental rates on H100/H200 class hardware will reprice downward as operators compete for utilization against a market where newer hardware produces orders of magnitude more output at the same power cost. The stranded asset isn't theoretical — it's hardware being delivered today against a depreciation schedule that assumed 5 years of competitive relevance.RAM (DDR5)
Prices will be lower than the February 2026 peak. The 400% ramflation was driven by AI factory demand absorbing total DRAM production capacity.
As HBM4 and SOCAMM displace DDR5 for the highest-demand AI workloads, and new fab capacity from Micron's Hiroshima expansion comes online in 2027, the DDR5 market should see meaningful relief. The exact magnitude depends on whether consumer and enterprise non-AI demand recovers the slack, or whether the market overshoots into a glut. Directionally: lower, timeline and depth uncertain.NVMe
SOCAMM LPDRAM handling warm KV cache in-flight reduces the NVMe use case to cold KV archival only — sessions idle for hours or days.
This is a significant demand reduction for the high-performance NVMe tier. The workload that justified $50-80/TB NVMe pricing — fast random-access KV staging — is being absorbed by the SOCAMM tier on-board. What remains for NVMe is large-block sequential cold storage, a workload that does not require NVMe's random-access performance premium. Expect pricing pressure on datacenter NVMe as the AI workload profile shifts.HDD
Inconclusive near-term, but NVIDIA has drawn the boundary for us.
The Vera Rubin POD architecture defines four explicit memory tiers [13]:
| Tier | Hardware | NVIDIA product | Latency |
|---|---|---|---|
| Hot KV | HBM4 on Rubin GPU | ✅ | Nanoseconds |
| Warm KV | SOCAMM 2TB on Vera CPU | ✅ | Microseconds |
| Warm-cold KV | STX rack NVMe via BlueField-4 | ✅ | Milliseconds |
| Cold archive | — | ❌ | Seconds+ |
NVIDIA's BlueField-4 STX rack extends GPU memory into NVMe for active KV reuse — sessions resuming within hours. It is not designed for day-scale or month-scale retention. The cold archive tier is explicitly outside the POD boundary, and NVIDIA has no product there.
At Vera Rubin's 700M tokens/sec throughput, even a 1% session persistence rate generates petabytes of cold KV and artifact data per day per POD. The I/O profile — large sequential writes, infrequent full-block reads, latency tolerance in seconds — is exactly where HDD is cost-optimal versus NVMe.
S3-compatible object storage on CMR nearline HDD, positioned as the cold archive tier that the NVIDIA POD architecture intentionally leaves unaddressed.
The hardware still works. It's just being priced out of existence — on a 24-month cycle instead of a 12-year one.
Question for longtime hosts: Do you still have 2012-2014 CPU models in production?
Comments
Prices always go up. For everything. It's been this way for a hundred plus years.
Good use of (possible) AI generated content along with modifications to make it look nicer.
Back on your topic, as far as major companies are concerned, AI is the future. They need to invest as much as they can to stay ahead of the curve. If they dont, they are gonna lose business to others who do. There is a risk of AI bubble crashing, but unlike the .com bubble, it is NOT dependent on regular users since large corpos are buying AI among each other, so the risk is somewhat mitigated. Moreover with each countries government stepping in, the AI bubble crash risk is mostly mitigated.
To grow the AI, more investment is needed in infrastructure, specifically datacenters, high-performance GPUs, networking, and power delivery. All of this has a direct impact on hardware pricing. When hyperscalers start buying GPUs and CPUs in massive quantities, it creates supply pressure that trickles down to the consumer market. We've already seen this with GPUs becoming harder to get or inflated in price during peak demand cycles.
Another factor is that AI hardware is not just about raw compute anymore, it's about efficiency. Companies are pushing for specialized chips (ASICs, NPUs, TPUs), which require new manufacturing processes and R&D. That cost has to be recovered somewhere, and it often shows up as higher prices across the board, even for non-AI hardware.
On top of that, supply chain constraints and geopolitical factors play a role. Advanced chips rely on a very small number of manufacturers and cutting-edge fabs. Any disruption, whether political or logistical, can impact global pricing.
That said, there is also a counter-effect. As production scales and competition increases, prices may stabilize or even drop for certain tiers of hardware. Not everyone needs top-tier AI chips, so older or mid-range hardware could become more affordable over time.
In short, expect high-end hardware prices to stay elevated (or even increase) due to AI demand, while lower-end and second-hand markets may become more attractive for regular users.
If you want information, feign ignorance reply with the wrong answer. Internet people will correct you ASAP!
It’s OK if you disagree with me. I can’t force you to be right!
AI is very confident when saying nonsense.
The very first post is by a new member.
Clearly AI-written.
Is it worth taking the time to read and effort to figure out what nuisance AI got wrong enough to make one draw the wrong conclusions?
Should we draw the line regarding AI posts and where - it is getting difficult (sometimes next-to-impossible) to tell if stuff was AI written.
I need to replace bearings in my platform pedals now.
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But why post it here, instead of on their (lowend-hosted) blog? If I wanted to read AI drivel, I'd open Linkedin instead of Lowendspirit.
... Where you could even wonder whether it is an actual person creating an account, prompting an LLM, copy/pasting the output or an agent sent out to create accounts at various fora and post useful-looking textvomit before pivoting to nefarious uses.
Long term what is the worry:
Hardware costs, or the cost of energy to power up that hardware??
I believe it is the latter.
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