Which desktop should you buy for local AI, and how much do you really need to spend?

Which desktop should you buy for local AI? The one whose memory fits the biggest model you'll really run, and for most people that costs far less than an "AI desktop". A PC you already own plus a 16GB graphics card runs 14B models. A 32GB RTX 5090 runs 32B models fast. You only need a 64GB to 128GB unified-memory desktop (Mac Studio, AMD Ryzen AI Max, NVIDIA GB10) for 70B and 120B-class models, and those cost thousands. If you don't know yet, rent a GPU by the hour first. Prices below are from Apple, Dell and NVIDIA's own US pages, read Nov 6, 2026.

How much memory do you need for local AI?

Enough to hold the model's weights plus some room for context. That is the whole buying decision; speed comes second. The numbers below are weights only at Q4_K_M, the community-default 4-bit format, using the 4.85 bits per weight our VRAM calculator uses. Context (the conversation the model remembers) adds more on top, so leave headroom.

Check your exact model and context length in the VRAM calculator, or open the model's own page: Llama 3.3 70B, gpt-oss-120b, Qwen3 32B. Our fuller guide is how much VRAM you need to run AI models.

Which desktop should you buy at each budget?

Here's the honest ladder, cheapest first. Every price is from the maker's own US page on Nov 6, 2026. Prices for memory-heavy machines have moved several times in 2026, so check the vendor page on the day you buy.

Under $500: add a 16GB graphics card to the PC you have

This is where most people should start. NVIDIA lists the RTX 5060 Ti family from $379 on its own page (the family includes an 8GB card; buy the 16GB one, which costs more). 16GB holds a 14B model at 4-bit with room for context, which covers most chat, coding-helper and summarizing work. Our best GPU for local AI guide compares the cards, and a used 24GB RTX 3090 is the other common route: see the used RTX 3090 buying guide. Building from scratch? The local AI PC under $1,000 guide has a full parts list.

ASUS Prime GeForce RTX 5060 Ti 16GB

16GB of GDDR7 with DLSS 4 and Multi Frame Generation, per NVIDIA. It is the 16GB version, not the 8GB one, which is the one that matters for 14B models.

Best for: 1440p gaming, and local AI that needs 16GB of VRAM on a budget.

Specs, AI models it runs and rental prices →

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About $2,000: an RTX 5090 for the fastest 32B models

NVIDIA's page lists the RTX 5090 with 32GB of GDDR7 and 1,792 GB/sec of memory bandwidth, starting at $1,999 for the card alone. Store prices for high-end cards have run above NVIDIA's list in 2026 (why GPU prices rose). 32GB holds a 32B model at 4-bit with plenty of context, and the bandwidth is why it generates text much faster than any unified-memory box on this page. What it can't do: hold a 70B model (about 40 GB of weights) on one card. See is the RTX 5090 worth it for local AI.

$2,500 to $8,000: unified memory for 70B and 120B models

This is the "AI desktop" tier everyone is talking about. These machines share one big pool of memory between CPU and GPU, so 128GB can hold models no single consumer card can. The trade: they generate tokens more slowly than an RTX 5090 on models that fit both.

• Mac Studio (Apple). From $2,499 with the M5 Max and 36GB; Apple's spec page lists 48GB, 64GB or 128GB on the 40-core M5 Max (614GB/s). The quietest way into 70B models. Compare Macs in the best Mac for local AI, and the smaller Mac mini buying guide.
• AMD Ryzen AI Max+ desktops. HP says the Z2 Mini G1a takes up to 128GB of unified memory and lets you assign up to 96GB to the GPU. Framework's Desktop offers 32GB, 64GB, 128GB and 192GB options, and its own table shows a 128GB Ryzen AI Max+ 395 running DeepSeek V4 Flash at a 2-bit quant in 90.9GB. We couldn't load a US price from Framework's store on Nov 6, 2026, so check its configurator. Windows and Linux both work. More in how much VRAM Strix Halo really gives you.
• NVIDIA GB10 (DGX Spark and partner versions). NVIDIA offers 64GB or 128GB, says the 64GB version is sold only through OEM partners, and claims inference on models "up to 200 billion parameters." Dell's US store listed its 128GB version with a 2TB SSD at $7,901 on Nov 6, 2026; choosing 64GB took $2,150 off. Pick this if you want NVIDIA's CUDA software stack in a small box. Our take: is the DGX Spark worth it.

$5,500 and up: 200B-plus models or serving a team

Apple's Mac Studio M5 Ultra starts at $5,499 with 96GB and goes to 256GB or 512GB with 1.2TB/s of bandwidth, the most memory in a single quiet desktop. Past that are workstation towers with several RTX PRO 6000 cards: StorageReview's leaderboard lists the Dell Precision 7875 with 192GB of GDDR7 across two cards and the HP Z8 Fury G6i at up to 384GB. Those make sense for multiple users or production serving, not for one person chatting with a model. Before buying even one card, read is the RTX PRO 6000 worth it for local AI.

What did the lab test actually rank?

StorageReview's Best Desktops for Local AI leaderboard (published Aug 13, 2026, marked updated Oct 24, 2026) scores systems on "a composite of vLLM online serving throughput, time-to-first-token, time-per-output-token, MAMF compute efficiency, GDSIO storage performance, and street price." Its picks:

• Best Overall Deskside AI System: NVIDIA DGX Spark (128GB). StorageReview calls it "the reference point every other deskside AI box is measured against."
• Best GB10 Implementation: Acer Veriton GN100.
• Best x86 Alternative: AMD Ryzen AI Halo (Strix Halo). In StorageReview's words, "in our testing it handled 200B-parameter-class models."
• Best Without a Discrete GPU: HP Z2 Mini G1a, which "ran GPT-OSS 120B with no discrete GPU at all."
• Best GB300 System: MSI XpertStation WS300 (748GB coherent memory), with the ASUS ExpertCenter Pro ET900N G3 as the alternative.
• Towers: Dell Precision 7875 (Best Tower for Local AI), HP Z8 Fury G6i (Best Multi-GPU Platform), Comino Grando with eight RTX PRO 6000 cards (The Extreme Pick).

The leaderboard also notes that NVIDIA "launched a 64GB DGX Spark SKU on October 23 at $4,999," sold through OEM partners. Read the ranking as "best at serving big models," which is a different question from "best value for one person at home." A box can win the first and still be the wrong buy for the second.

Should you rent a GPU before you buy?

Yes, if you aren't sure which model size you'll settle on. On Nov 6, 2026 at 21:20 UTC, our price tracker's cheapest single-GPU on-demand listings on Vast.ai were $0.44/hr for a 32GB RTX 5090 and $0.10/hr for a 24GB RTX 3090; Runpod listed the RTX 5090 at $1.19/hr. Those are marketplace prices and they move hourly, so see /cloud-gpu for today's numbers. The simple math: at $0.44/hr, the $1,999 RTX 5090 list price buys about 4,500 hours of rented 5090 time, and Dell's $7,901 GB10 desktop buys about 17,800. Rent for a week of real use, note the model and context you actually needed, then buy the cheapest machine that fits it.

Prebuilt AI desktop or build your own?

If the answer is "a graphics card," adding it to a PC you already own is the cheapest path by far, as long as your power supply and case fit the card. The unified-memory machines (Mac Studio, Ryzen AI Max boxes, GB10) can't be built from parts in the same way, because the memory is soldered to the chip, so you buy them as complete systems and pick the memory up front. You can't add more later. The trade-offs are in prebuilt vs DIY local AI PC.

What this page can't tell you

• Speed on your exact model. We haven't run these machines ourselves. Speed numbers come from the vendors and StorageReview's lab; your tokens per second depend on model, quant, context length and software.
• Street prices tomorrow. Memory-heavy hardware prices changed several times in 2026. Every price here is the vendor's US page on Nov 6, 2026.
• Low-bit quality. Squeezing a huge model into memory with a 2-bit or 3-bit quant (as in Framework's own table) runs, but answers get worse. A smaller model at 4-bit is often the better choice.

None of this is a call to buy the biggest box you can afford. Find the model you'll really run, check its memory in the VRAM calculator, rent it for an hour on /cloud-gpu if you're unsure, then buy the cheapest machine that holds it.