Best Local AI Server 2026: Plug-and-Play, Build-It-Yourself and Rackable Picks

The best local AI server for most people is a used-RTX-3090 build at about $1,500, because 24GB of VRAM at 936GB/s runs every model up to the 32B class fast and nothing near that price matches it. If you will not build, the best plug-and-play local AI server is the Minisforum MS-S1 Max, a 128GB Ryzen AI Max+ 395 box at $3,919 on November 2, 2026, or a Mac Studio M5 Ultra (96GB, from $5,499) if you want that class of memory at more than four times the bandwidth. The cheapest server that runs a 70B at speed is two used RTX 3090s in a 4U chassis. Zanus AI, the appliance many searchers are comparing, is an 8U quote-only business product and is covered fairly below.

This page picks the machine; the build guides build it. When a DIY row wins for you, the homelab AI server build and the $1,500 dedicated AI server build carry the parts lists and assembly steps. If what you actually want is a desktop you also work on, that is the best PC for local LLMs guide, and the wider hardware question lives on the local AI hardware hub. The running-cost comparison against ChatGPT, with the arithmetic shown, is in the cost section.

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Which local AI server is best? The picks at a glance

Every figure on this page is a manufacturer specification or a price read from the vendor's own store or the NVIDIA marketplace on November 2, 2026. Nothing here is a first-hand benchmark, and the tokens-per-second numbers are bandwidth ceilings (published memory bandwidth divided by the Ollama file size), not measurements.

The rest of the page is the reasoning per row, starting with the single number that separates them.

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What actually decides how fast a local AI server is?

Memory bandwidth, not teraflops. Generating one token from a dense model means reading every weight once, so the hard ceiling is bandwidth divided by the size of the model in memory. That one division explains almost every disappointment people have with local AI hardware, and it is why a $6,950 box can be slower than a used graphics card on the same model.

Work through it with a 70B. Ollama lists llama3.3:70b at 43GB (Q4_K_M). Divide each machine's published bandwidth by 43GB and you get its ceiling on a 70B:

Sources: NVIDIA's GeForce specification comparison for the 3090 and 5090, the Tesla P40 datasheet, AMD's Ryzen AI Max+ 395 platform page, the DGX Spark product page for GB10, and Apple's Mac Studio tech specs for the M5 figures.

Read those numbers as ceilings, not forecasts. Real throughput always lands under them, because attention overhead, KV cache reads and the runtime all take a cut, but nothing gets you above them and no software update will. (On the one machine this site has measured, an M3 Pro laptop, 7-8B models reached roughly 80 percent of their ceiling; the table is on the PC build guide.) Two things fall straight out of the table:

• A 128GB box is a capacity purchase, not a speed purchase. At 256-273GB/s, the same 70B that a pair of 3090s would push at roughly 22 tok/s is capped near 6 tok/s. Where the 128GB boxes shine is mixture-of-experts models, which read only their active parameters per token: Ollama's qwen3.6:35b-a3b is a 23GB file that activates about 3B parameters per token, and gpt-oss:120b is a 65GB file that only a 128GB box or a 96GB Mac can hold at all.
• A vendor that will not publish bandwidth or VRAM cannot be placed on this table. That is not a criticism of the product, but it does mean you have to ask before you sign.

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Which prebuilt local AI server should you buy?

Buy a prebuilt for one of two reasons: you want memory capacity no consumer GPU has, or you want a finished box with someone to call. The first reason points at the 128GB unified-memory machines and the Mac Studio; the second is what Zanus sells. Here is the field with what each vendor actually publishes, priced on November 2, 2026.

Prices in this category have moved by thousands of dollars inside 2026, and only upward. NVIDIA raised the DGX Spark from $3,999 to $4,699 in February citing memory supply, and its own marketplace lists the 128GB unit at $6,950 today. The Beelink was reviewed at "$1999 or less" in October 2025 and lists at $4,349 now. The Minisforum went from $3,799 at this page's September check to $3,919, and its "regular" price rose from $4,749 to $4,899. Treat every number above as a snapshot and read the vendor's page the day you buy. The three-way comparison of DGX Spark, Strix Halo and Mac Studio goes deeper on what each refuses to run.

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What is the best plug-and-play AI server?

The Minisforum MS-S1 Max is the best plug-and-play local AI server on November 2, 2026, at $3,919 for 128GB of unified memory and a 2TB drive, because it is the cheapest 128GB box you can actually order and it needs no driver work. It ships with Windows, Ollama and LM Studio install in minutes, and it has dual 10GbE so it can sit on a rack shelf and serve the rest of the house. The same chip in the Beelink GTR9 Pro costs $430 more and is on a 35-day pre-order; the Framework Desktop was out of stock in every configuration.

Three other plug-and-play answers, for three different buyers:

• Fastest: Mac Studio M5 Ultra, from $5,499. 96GB at 1.2TB/s gives a 70B a ~28 tok/s ceiling, better than a dual-3090 rig, from a silent box with one cable. It is $1,580 more than the Minisforum for 32GB less memory, and you pay that for bandwidth. Apple's tech specs rate it at 480W maximum continuous, and it idles far below that.
• Cheapest plug-and-play that is still useful: Mac Studio M5 Max, from $2,499. 36GB base at 460GB/s holds the whole 27B-32B class (qwen3.6:27b is a 17-19GB file) and generates it faster than a 3090 could, for less than the 128GB boxes. The 64GB configuration is the one to order if you want a 70B to fit at all.
• CUDA-first: NVIDIA DGX Spark at $6,950, or the ASUS Ascent GX10 at $5,999. Same 128GB and 273GB/s as the Strix Halo boxes, with DGX OS and the CUDA stack preinstalled. If your workflow is vLLM, TensorRT or anything that assumes an NVIDIA driver, this is the only box in the class that runs it natively. It is also $2,000-$3,000 more than the Minisforum for the same memory and six percent more bandwidth, which is why it is not the default pick. The DGX Spark review covers what it does well.

And one non-answer: Zanus is plug-and-play for a business, not for a person. It arrives as an 8U rack unit on four dedicated circuits with a software suite and a support contract, by quote. The next section sets out exactly what the vendor does and does not publish.

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What is Zanus AI, and is it the best local AI server?

Zanus AI sells a turnkey, quote-priced private AI appliance for businesses: an 8U rackmount server with its own bundled software suite, support and financing, rather than a bare box you load Ollama onto. If your search was "best local AI server" and you meant a machine for yourself or a small technical team, it is the wrong size of product. If you meant "one vendor who delivers a working private AI system with a CRM, document search and a chatbot, and answers the phone", it is the category you are shopping in, and the honest comparison is against other managed appliances, not against a mini-PC.

What the vendor publishes, from its own product pages:

• Three tiers. Prime ("entry local AI server for smaller teams and document libraries", 2,000,000+ documents), Quantum ("balanced on-prem AI system for multi-user RAG and workflows", 5,000,000+ documents, extended context windows) and an Enterprise Cluster of Prime and Quantum nodes for "large concurrency".
• The chassis. 8U, standard 19-inch rack, 14 inches tall, about 120 lb. Air cooling, no liquid. Redundant power and network.
• The power. Four IEC C20 inlets. "Peak consumption 6 kW" and "idle consumption ~1 kW", in the vendor's own words. That is four dedicated circuits, not an office power strip; budget for an electrician before the delivery date, and see the cost section for what 1 kW of idle costs per month.
• The software. The Zanus AI operating system with business modules: AI chat and team messaging, client and customer management, calendar and task management, document and report generation, marketing automation and a website chatbot, a vector store, role-based access control and an audit trail. "Every server includes one industry-specific AI software package of your choice." Air-gap and offline operation are supported. The vendor describes the product as "a fixed asset with a defined one-time cost".
• The commercial terms. Custom configured, custom quoted, financing available up to 60 months.

What the vendor does not publish, and what you should get in writing before you sign a quote:

1. The GPU model, the number of GPUs and the total VRAM. The pages say "enterprise-grade AI inference GPUs" for Prime and "enhanced enterprise GPUs for extended context inference" for Quantum, and nothing more. Without VRAM and bandwidth you cannot know which model sizes it holds or how fast it will answer; ask for both numbers and the row it would occupy in the bandwidth table above.
2. Which language models it runs, by name and parameter count. Zanus says it runs "multiple LLMs built-in", and that they are not GPT, Copilot, Grok or Claude. Ask whether they are fine-tunes of open models such as Llama or Qwen, what the context window is, and whether you can load your own GGUF or safetensors weights.
3. The price, and what recurs. There is no public price list; every tier on the collection page reads "Request Quote". Third-party sites publish estimates and say themselves that Zanus has not confirmed them, so do not plan around any number you did not get from the vendor. Ask what the one-time price includes, what optional annual maintenance covers, and what happens to updates and the software licence if the support contract lapses.
4. The exit. If the appliance is ever retired, can the document store and the model weights be exported into a standard runtime such as Ollama or vLLM on your own hardware?

When Zanus makes sense: a business with compliance requirements, more users than one machine's worth of enthusiasts, and no one on staff who wants to own the runtime, where the software modules and the support contract are the product and the GPUs are a detail. When it does not: an individual, a developer or a small technical team, for whom the same open models run on a $2,500-$7,000 box in the table above with a published spec sheet and a fraction of the power draw. The self-hosted AI cost calculator and the local AI vs ChatGPT cost comparison go further on the subscription side of that decision.

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Which 128GB box or Mac Studio should you buy?

For an individual who will not build, the choice is between a 128GB Strix Halo mini-PC and a Mac Studio, and the bandwidth table decides it. The mini-PC gives you the most model memory per dollar; the Mac gives you the same class of memory at more than four times the bandwidth, silently, for more money.

The 128GB Ryzen AI Max+ 395 boxes all share one chip, which AMD's platform page specifies as 16 Zen 5 cores, a Radeon 8060S, 128GB of LPDDR5x at 8000 MT/s, 256GB/s of memory bandwidth and a 120W TDP. The vendors differ on ports, cooling budgets and, in 2026's memory shortage, wildly on price and stock. On November 2, 2026 the Minisforum MS-S1 Max was the one you could order ($3,919, estimated shipping early November, dual 10GbE), the Beelink GTR9 Pro was $4,349 on a 35-day pre-order, and the Framework Desktop was out of stock in every configuration. What 128GB buys you is the class above a single GPU: 70B dense models load easily, and 100B-class mixture-of-experts models, which read only a fraction of their weights per token, are where these boxes feel fast. What it does not buy is speed on dense 70B models, which cap near 6 tok/s. The box-by-box comparison is on the best Strix Halo mini-PC page, and the chip itself, including the BIOS memory split, is explained in the Strix Halo guide.

The GB10 boxes (NVIDIA DGX Spark, ASUS Ascent GX10, MSI EdgeXpert) are the same idea with NVIDIA's chip: 128GB at 273GB/s, DGX OS and the CUDA stack preinstalled, a 240W supply and a 140W chip. They are the pick if your workflow is CUDA-first. They were the value pick for that when the ASUS clone sold for about $3,000 at launch; at $5,999 for the GX10 and $6,950 for the DGX Spark on the NVIDIA marketplace today, you are paying $2,000-$3,000 more than the Strix Halo boxes for six percent more bandwidth and the driver stack. The DGX Spark review covers what the platform does well and where it stalls.

The Mac Studio is the fastest plug-and-play local AI server on this page and the only silent one that holds a 70B at speed. Apple's August 2026 refresh moved the line to the M5 Max (36GB base, 48, 64 or 128GB, 460GB/s on the 32-core GPU and 614GB/s on the 40-core, from $2,499) and the M5 Ultra (96GB base, 256 or 512GB, 1.2TB/s, from $5,499). Divide 1.2TB/s by 43GB and a 70B has a ~28 tok/s ceiling on the Ultra, ahead of a dual-3090 rig, from a machine with one cable and no fan noise to speak of; at 512GB it is also the only consumer-shaped machine that can attempt the frontier-class open models. The M5 Max at 64GB or more is the quieter, cheaper middle: a ~11-14 tok/s ceiling on a 70B, and comfortably fast on everything smaller. One sizing note before you order: macOS does not hand the whole pool to the GPU by default, so plan on roughly three-quarters of installed memory being addressable unless you raise the limit. Configs and the buy-more-memory rule are in the Apple Silicon AI buying guide; the head-to-head against a GPU tower is in Mac Studio vs PC build.

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What is the best local AI server to build yourself?

If you will build it and you do not have a niche requirement, build around a used RTX 3090. 24GB of VRAM is the gate that matters, the cheapest tier that runs the current best single-GPU models at full Q4 quality, and it pairs that capacity with 936GB/s, which nothing else near the price does. No current GeForce card offers 24GB below the RTX 5090's 32GB, and on November 2, 2026 the 5090 was out of stock at its $1,999 Founders Edition price on NVIDIA's own marketplace while partner cards listed at $3,340-$6,599; the 3090 is not just the budget route to 24GB, it is the only one.

What 24GB actually buys you, cross-checked against the sizes Ollama publishes and the 24GB VRAM model picks:

• Qwen3.6 27B: Ollama lists qwen3.6:27b-q4_K_M at 17GB (the default 27b tag is 18-19GB), leaving real headroom for a working context window. 936 / 17 = a ~55 tok/s ceiling. Full breakdown on the Qwen3.6 27B page.
• Qwen 2.5 Coder 32B: qwen2.5-coder:32b is listed at 20GB, a ~47 tok/s ceiling, and it is the reason serious builds target 24GB at all.
• DeepSeek-R1 32B: deepseek-r1:32b is also 20GB, full 32B-quality reasoning with visible chain-of-thought.
• Everything smaller is comfortable. llama3.1:8b is 4.9GB, so a 3090 holds it almost five times over and the ceiling is near 190 tok/s.

There are two recipes for this machine on the site, and they are genuinely different builds:

The homelab AI server build is the budget path: three tiers at $960, $1,200 and $2,300 as specced there, on a used DDR4 platform (Ryzen 5700X or i5-12400F class) with a marketplace 3090. It is the tier to pick when the server runs a few hours a day rather than around the clock.

The $1,500 dedicated AI server build puts the same GPU on a current DDR5 platform: a Ryzen 7 7700 (65W, chosen deliberately for low 24/7 idle draw), 64GB of DDR5-6000 and a quality 850W PSU. The extra system RAM is the point: when you occasionally step past 24GB, layers spill to system memory instead of failing outright, and large-context RAG work has room to breathe. One warning that did not apply when that guide was costed: DDR5 is the line that has moved most. On November 2, 2026 a 32GB DDR5-6000 kit was $479.99-$509.99 at Walmart and a 64GB kit $833.63-$1,029.99, so re-price the memory line before you trust the total.

Which of the two? If money is tight and the box sleeps at night, the homelab tiers. If it runs around the clock and you want the platform to last five years, the $1,500 build. Both hit an identical model ceiling, because the ceiling is the GPU.

One honest boundary: neither build is the cheapest possible local AI machine. A used office PC with a GTX 1060 runs 7B models for roughly $150-$220 (the $200 budget local AI machine guide covers that tier), a 16GB card covers the whole 14B class (see what a 16GB card runs), and a general-purpose desktop is a different question again (the best PC for local LLMs guide). This page is about servers, machines that sit on your network and serve models, where the 3090 tier is the honest floor.

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What is the best budget AI dedicated server?

The best budget dedicated AI server is the $960 homelab tier: a used DDR4 platform with a used RTX 3090, built from the homelab AI server build. It is the cheapest machine on this site that reaches 24GB of VRAM without turning into a project, and 24GB is where the useful single-GPU models live.

If even that is too much, the ladder below it, from cheapest to most capable, with what each rung actually runs:

The reason the 3090 tier is still the recommendation and not the 5060 Ti: the new 16GB card now costs roughly what a used 3090 did when the homelab guide was costed, and it buys you a third less memory at half the bandwidth. The memory shortage has inverted the usual advice that new beats used. Price per gigabyte across every card and box is worked through in the GPU price-per-GB guide.

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What is the cheapest rackable or multi-GPU local AI server?

Two used RTX 3090s, pooled to 48GB, in a 4U GPU chassis. The pair was costed at $1,700-$2,100 in the cheapest 70B build guide and used prices have not fallen since; the platform around them needs a bigger PSU, a board with real dual-slot spacing and a case with front-to-back airflow, which is exactly what a 4U rack chassis is.

The maths is unforgiving and worth internalising. A 70B at Q4_K_M is 43GB of weights by Ollama's listing. No 24GB card holds it. No 32GB card holds it either, which is why a $3,340 RTX 5090 cannot run a full-quality 70B on its own. 48GB of pooled VRAM is the floor. Splitting the weights across two 3090s means each card reads its own half at 936GB/s, so the pair keeps the same ~22 tok/s ceiling a single 3090 would have if it could hold the model. That is the best 70B ceiling available anywhere near this money, and it beats every 128GB box on the page by more than three to one.

Why the rack, specifically: two 350W cards want a chassis that pulls air in the front and out the back rather than a tower that recirculates it, and a 4U case gives you full-height slots, room for a 1000W-class PSU (Corsair's RM1000x was $184.99 at Walmart on November 2, 2026) and a place to bolt it that is not under your desk. It is also the cheapest form factor that grows: the same chassis takes a third card later, and the wiring lesson from the Zanus spec sheet applies at a smaller scale, so put the rig on its own circuit and know its wall draw before you fill the slots.

Skip this rig if you do not have a concrete 70B need. A single-3090 server running Qwen3.6 27B covers a surprising share of what people imagine they need a 70B for, and it does it at a far higher bandwidth-to-model-size ratio. The full comparison against a single 5090 and a Mac Studio, including when each of those wins instead, is in the cheapest 70B build guide.

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Private AI server vs ChatGPT: what is the total cost of ownership?

Over three years, a $1,500 used-3090 private AI server costs about $2,100 including electricity, or $58 a month. That is more than one ChatGPT Plus seat ($720 over three years) and far less than one ChatGPT Pro 200 seat ($7,200) or a five-person ChatGPT Business workspace ($4,500). The server pays for itself when two or more people share it, when you would otherwise need a Pro tier, or when the data cannot leave the building. One light user on Plus should keep paying $20.

Every input is published and dated; the two assumptions (hours of use and idle draw) are labelled so you can change them.

The server side. Electricity is the only recurring cost. The US average residential rate was 18.16 cents per kWh in August 2026 (EIA Table 5.6.A); use your own bill's rate if you have it. Assume four loaded hours a day and idle the rest; idle wattage is an assumption, not a spec, because no vendor publishes it for a DIY box.

The ChatGPT side, from OpenAI's own help centre and pricing pages on November 2, 2026:

Put the two tables together and the break-even is plain:

• One person, Plus: ChatGPT wins. $720 against ~$2,090 for the 3090 server, and the server's hardware alone exceeds three years of Plus. Buy the server for privacy, offline use or unlimited volume, not to save money.
• One person, any Pro tier: the server wins inside the first year. Pro 100 is $1,200 a year; the 3090 server's three-year total is under two years of Pro 100 and under a single year of Pro 200.
• Two to five people, Business: the server wins. Five Standard seats are $4,500 over three years on monthly billing against ~$2,090 for a server everyone shares, and the gap widens with every seat. A 128GB Minisforum at ~$4,090 comes in just under five Business seats and runs models a chat subscription cannot.
• API workloads: compare tokens, not seats. At the 20M-in, 5M-out example above the API costs $3,240 over three years, so a $2,090 server wins; at a tenth of that usage the API costs $324 and the server loses. Your own token count decides it.
• Zanus: cannot be placed. With no published price and a vendor-stated 1 kW idle, the electricity alone is about $130 a month at the US average rate, so the quote has to be weighed against roughly $4,700 of three-year idle power before the hardware number is even known.

What the arithmetic leaves out, deliberately: your time to build and maintain the box (real, and yours to price), the model-quality gap between open 27B-70B models and the frontier models a subscription buys (real, and task-dependent), and the resale value of a GPU after three years (real, and in your favour). The self-hosted AI cost calculator lets you put your own rate, hours and seat count into the same formula.

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Should you buy a Tesla P40 for cheap 24GB?

The Tesla P40 is the cheapest 24GB in existence and it is still the wrong foundation for a server. It loads the same model sizes a 3090 does, then generates them at roughly a third of the speed, and it arrives as a project, not a part.

The specifics come straight from NVIDIA's own P40 datasheet: 24GB of GDDR5, 346GB/s of bandwidth, 250W board power, and 2016-era Pascal silicon with no Tensor Cores. That means no usable FP16 path and no Flash Attention, so prompt processing is slow in a way raw tok/s figures hide; long-context work feels worse than the generation numbers suggest. It is also a passively cooled datacenter card: you add a blower shroud and fan yourself, and it takes power through an EPS-style 8-pin rather than a normal PCIe plug. The full P40 breakdown walks through that build.

Where it honestly fits: a hobbyist box for 7B-14B models where dollars-per-gigabyte is the only metric and waiting is fine. Where it fails is the dense-70B dream. Two P40s give you 48GB, the 70B loads, and then 346GB/s against 43GB of weights caps you near 8 tok/s no matter what you do. If a 70B server is the goal, that money is a down payment on the dual-3090 rig above.

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Which one should you pick?

Match the server to the biggest model you will actually serve and to who will maintain it; every other spec follows.

• Under ~$500 and just exploring: do not build a server yet. A $200 used-PC setup proves out your use case on 7B models first.
• ~$1,000-$1,700 and willing to build: a single used-3090 server, the homelab tiers on a budget or the $1,500 dedicated build for 24/7 duty. Runs the 27B-32B class at the best bandwidth-per-dollar here.
• You will not build and want the most memory for the money: the Minisforum MS-S1 Max at $3,919, the one 128GB box shipping as of this update. Capacity, not speed.
• You will not build and want it fast and silent: a Mac Studio, the M5 Max at 64GB+ for the 32B class, the M5 Ultra for 70B at a ~28 tok/s ceiling.
• You concretely need 70B and can build: dual used 3090s in a 4U chassis, the highest 70B ceiling for the least money. Accept the PSU, the spacing and the noise.
• Your CUDA toolchain is non-negotiable and you will not build: a GB10 box, the ASUS Ascent GX10 at $5,999 while it is in stock, or the DGX Spark at $6,950.
• You are buying for a business and want one vendor, bundled software and a support contract: get a Zanus quote, and get the four answers in the Zanus section in writing first.
• Dollars-per-GB is your only metric and you like projects: one Tesla P40 for patient 7B-14B work. Not the 70B stack.
• You want a desktop you also work on, not a headless server: the best PC for local LLMs guide, three parts lists priced the same day as this page.

Whichever row you land on, the hardware hub has the component-level picture, and the per-tier model picks (24GB, 16GB, 12GB, 8GB) tell you exactly what to pull first on day one.

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Why do these prices keep moving?

Every price on this page is less stable than usual, in one direction: up. The memory shortage has pushed GPU street prices to roughly double their launch prices (an RTX 5070 Ti launched at $749 and listed at $1,269.99-$1,499.99 on the NVIDIA marketplace on November 2, 2026; an RTX 5060 Ti 16GB launched at $429 and listed at $799.99-$879.99), used cards moved with them, DDR5 kits roughly doubled, and the soldered-LPDDR5X mini-PCs and the GB10 boxes have been repriced by their own makers more than once this year. The DGX Spark has gone $3,999 to $4,699 to $6,950 inside twelve months.

The concrete effect on this roundup: the build guides remain accurate as recipes, but their GPU and memory lines are the ones to re-price from completed listings, not asking prices, the week you buy. The prebuilt table carries the date it was read for the same reason.

What this page deliberately does not say is that you should buy now before prices rise further. Nobody knows that. The practical advice is narrower: check the vendor's page the week you buy, and do not pay a panic price for any single machine when a different row of the table above sidesteps it entirely. The full picture of what is inflated, what is delayed and what is still fair value is in why GPU prices are so high right now.

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FAQ