Local AI hardware by budget: four rungs, and the first costs nothing

Before moving up a rung, try the Qwen3-8B context-memory example with your own loaded-weight and runtime allocations. It covers that model’s 16-bit KV cache in one memory pool, not the larger models or multi-card setups below, and predicts no speed. If the October whole-PC shortlist fits the job, use its full-cost worksheet to add tax, shipping, required extras and any new membership cost before comparing it with your budget; older asks below keep their original dates.

Based on figures already published on Hard Bargains pages, carried forward with their original dates and caveats, not on our own testing.

Local AI buying advice starts with a machine. Buyers start with a number. Four numbers cover almost all of them, and the first rung is the one to rule out before spending anything: the computer already on your desk runs some size of model, and finding out costs nothing.

The short answer

The same four rungs in prose

• $0 is a real rung, not a courtesy. A 16GB Apple Silicon Mac leaves about 10.7GB for a model after macOS and the Metal driver take their shares, which runs an 8B model at about 4-bit comfortably. Establish that before reading further.
• Under $500 is a PC-only rung. Apple Silicon memory is soldered, so on a Mac the fix is a different Mac. On a PC it is a used 12-16GB card, or more system RAM so a mixture-of-experts model can keep its expert layers off the card.
• $800 to $1,400 is the only real fork on this page: a used RTX 3090 with 24GB against a used Mac mini with 16GB. The card holds more and generates faster; the mini is a whole computer and needs no power supply, case room or slot spacing.
• $2,000 to $3,500 buys capacity, not speed. A 128GB Strix Halo box prices memory at $27 a gigabyte and moves it at 256GB/s; two used 3090s hold 48GB with real power and case conditions; a 36GB Mac gives up capacity for bandwidth.
• A 70B dense model first becomes possible on the top rung. The 128GB box measured 4.5 t/s on a 70B, on a Framework Desktop with the same chip; two used 3090s measured 16.29 t/s in a May 2024 run, and the Mac route has estimates only.

Where each rung ends

Where each rung ends: the exact next step for each budget

Supports $0: keep what you own · Under $500, on a PC · $800-$1,400, with a desktop PC: more model, faster · $800-$1,400, as a whole computer · $2,000-$3,500: 128GB, capacity over speed · $2,000-$3,500 on a Mac: speed over capacity

Rung	Exact next step
$0	Check what your computer runs; nothing to buy
Under $500	A used 12GB card such as an RTX 3060, from eBay, r/hardwareswap or local pickup; we hold no dated price for it. 16GB is above this rung: the cheapest dated used 16GB listing was $545 (RTX 5060 Ti 16GB, GPUDojo, Sept. 15) and the lowest new one $790 (Newegg, same day), so the new card linked in the under-$500 section is not an under-$500 route
$800-$1,400	A used RTX 3090 from eBay or r/hardwareswap, offered near the $1,032 sold price (GetPCParts, Sept. 12); or a used Mac mini checked against the price ladder
$2,000-$3,500	The 128GB EVO-X2 from GMKtec’s store, $3,499.99 with 1TB (Sept. 16), or the 128GB/2TB model on Amazon, a link to one exact product, title checked Sept. 16, with no Amazon price quoted. For a 70B on a Mac, the 64GB M5 Pro mini, ordered from Apple

The four rungs

Supports $0: keep what you own · Under $500, on a PC · $800-$1,400, with a desktop PC: more model, faster · $800-$1,400, as a whole computer · $2,000-$3,500: 128GB, capacity over speed · $2,000-$3,500, with a desktop that takes two cards · $2,000-$3,500 on a Mac: speed over capacity

Every rung below carries the same five things: what it is, the largest model class it genuinely runs at about 4-bit, one measured speed with its harness and date, what it cannot do and the owned page that finishes the decision. Model minimums are canirun.ai estimates at roughly 4-bit quantization, not our measurements, and the recommended figures that site publishes are higher than the minimums quoted here.

$0: keep what you own

Supports $0: keep what you own

What it is. The machine you already have, measured honestly. Usable memory is total memory minus what the operating system and your open apps are holding. Apple Silicon subtracts twice: in llama.cpp’s discussion of the Metal memory split, developers report that macOS withholds a third of unified memory from the GPU below 32GB and a quarter above it, which leaves a 16GB Mac about 10.7GB to load a model into. That figure is what developers report, not an Apple specification.

What it runs. A 16GB Apple Silicon Mac runs Llama 3.1 8B at about 4-bit comfortably against its 4.5GB minimum, and clears Qwen3 14B’s 7.8GB minimum without reaching the 13GB canirun.ai calls recommended. An 8GB Nvidia card runs Llama 3.2 3B comfortably and an 8B at 4-bit with little room left for context. A 24GB card already reaches the 27B class, which is as far as any single card on this page goes; 32GB cards exist above it, at prices above the top rung here.

Measured speed. 24.11 t/s on an M4 and 37.87 on an M2 Pro, LLaMA 7B at Q4_0, from the community-maintained llama.cpp Apple Silicon table, checked Sept. 16, 2026. Each row is one contributor’s single run on one build, not a distribution, and none of them is a Hard Bargains test.

What it cannot do. A 16GB machine does not hold the 27B class with working room, and no machine on this rung approaches a 70B dense model’s 39.1GB minimum. Benchmark speeds also flatter every machine: in a measured DeepSeek R1 run, an 11,000-token prompt cut generation from a 16-18 t/s benchmark range to 4.26 under llama.cpp and 11.15 under MLX.

Finish the decision at can your computer run a local AI model?, which works out your usable number by machine and lists the fixes that cost $0: a lower quantization, a smaller model, a mixture-of-experts model, closing what holds the memory, partial offload and raising the Metal cap on a Mac. Work through that list first. If it ends with a model you can live with, the buying decision is already made, and nothing on this rung is for sale.

Under $500: more memory, on a PC

Under $500 in detail: what 12GB and 16GB run, the RTX 3060 speed, where our price custody is thin, and where to look

Supports Under $500, on a PC

What it is. On a PC, a used 12-16GB card, or more system RAM so a mixture-of-experts model can keep its expert layers off the card. On a Mac this rung does not exist: Apple Silicon memory is soldered, so the fix is a different Mac and that starts at the next rung. Whether the RAM route is worth taking now is partly a market question; when the RAM shortage ends collects what the named forecasters actually say.

What it runs. 12GB clears Qwen3 14B’s 7.8GB minimum but not its 13GB recommended figure. 16GB clears gpt-oss-20b’s 11.7GB minimum; Qwen 3.5 27B’s 15.5GB minimum leaves a 16GB card almost nothing for context or display use, so 27B is the next rung’s job, not this one’s.

Measured speed. 75.57 t/s on an RTX 3060 12GB and 90.94 on an RTX 5060 Ti 16GB, Llama-2-7B at Q4_0 with full GPU offload, from llama.cpp’s CUDA table on builds baa9255 and 89d1029; an RTX 4060 Ti 8GB measured 63.86 on build 89d1029. Checked Sept. 16, 2026, each one contributor’s single run. Those figures come from a different table, model and settings than the Apple Silicon figures above.

What it cannot do. Hold a 27B model with working room, or a 70B dense model at all. This is also the rung where our price custody is thinnest, and it is fairer to say so than to round: we hold no dated price for a used RTX 3060 12GB, and the cheapest dated used 16GB figure we have is a $545 RTX 5060 Ti 16GB eBay listing (GPUDojo, Sept. 15) — already above this rung’s ceiling. The lowest new listing for that card was $790 at Newegg the same day, about $49 a gigabyte by our arithmetic.

Finish the decision at which GPU runs your local model, which matches a model to 16GB, 24GB or 32GB with measured speeds, dollars per gigabyte and the power-supply, connector and length checks each card needs.

No Amazon price is quoted anywhere on this page, and a price quoted from a manufacturer, a store or a tracker is not an Amazon offer.

Find 16GB graphics cards on Amazon. This opens search results, not a specific product. Results mix the 8GB and 16GB versions of the same card name, so confirm the memory size on the exact listing.

See the ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC Edition on Amazon. This opens one exact product listing, whose title we checked Sept. 16, 2026; its seller, price, condition and stock have not been checked. It is a new card, and the lowest new listing we have a date for sits above this rung.

Other stores. Under $500 realistically means a used 12GB card, because the cheapest dated used 16GB listing was $545: eBay listings for the RTX 3060 12GB, r/hardwareswap and local pickup, where you can watch the card run before paying. For 16GB above this rung, GPUDojo tracks listings by store and Newegg’s results are another starting point.

$800 to $1,400: a used RTX 3090 or a used Mac mini

Supports $800-$1,400, with a desktop PC: more model, faster · $800-$1,400, as a whole computer

This is the only rung where two genuinely different machines answer the same question, so it gets a table rather than a recommendation.

	Used RTX 3090, 24GB	Used Mac mini, 16GB
What it is	A card for a desktop PC you already own	A complete computer
Price, dated	$1,032 eBay sold-listing price (GetPCParts, as of Sept. 12); $1,337-$1,463 fair asking range across 291 eBay listings (ResalePrices, Sept. 15)	$749 median ask and a $625 single-listing floor for a base M4 16GB/256GB across 134 offers (Sept. 2); 16GB/512GB medians of $949.99 for an M2 Pro and $958.87 for an M4 (Sept. 2)
Memory a model can use	24GB, less display use	About 10.7GB of 16GB
Largest class at about 4-bit	Qwen 3.5 27B at 15.5GB and Qwen3 30B-A3B at 16.8GB, both below their recommended figures	Llama 3.1 8B comfortably; Qwen3 14B above its minimum, below its recommended
Measured speed	158.16 t/s at 7B (CUDA table, build c76b420) and 161.77 on gpt-oss-20b (llama.cpp’s gpt-oss guide, build a094f38)	24.11 t/s on the M4 and 37.87 on the M2 Pro (Apple Silicon table, LLaMA 7B at Q4_0)
What else must be true	A desktop with a 750W supply and room: the Founders Edition is 313mm long and takes three slots, and partner boards differ	Nothing. Memory is soldered, so 16GB is this machine’s ceiling for its life
Route	eBay, r/hardwareswap and local pickup	Swappa, PayMore, eBay and Apple Certified Refurbished

The two speed cells come from different community tables run by different contributors on different systems. They are the closest published like-for-like figures, not a controlled comparison.

Neither side of this fork runs a 70B-class model. Llama 3.3 70B needs 39.1GB at about 4-bit; the card holds 24GB and the mini about 10.7GB. If a 70B is the requirement, stop here and read the next rung, where it first becomes possible — measured at 4.5 t/s on a 128GB box with the Strix Halo chip and at 16.29 t/s on two used 3090s in a May 2024 run, and on estimated speeds only for a Mac. Within what each machine does hold, the two axes separate cleanly: VRAM decides what fits and memory bandwidth decides how fast it generates, and the 3090’s 24GB on a 384-bit interface wins on both against a 120GB/s M4 mini. What the mini wins is everything that is not the model. It is a whole computer rather than a component, it needs no power supply, case room or slot spacing, and it is the only side of this fork that is also a desk machine afterward.

Bandwidth explains the mini’s own split, too. The 200GB/s M2 Pro generated 37.87 t/s against the 120GB/s M4’s 24.11, at a Sept. 2 median ask $8.88 lower; the comparison of those two against the new M6 covers processor speed, ports and the new-machine premium.

On the card side, offer near the sold price rather than the asks. The $1,032 completed sale and the $1,337-$1,463 asking range differ by more than 20%, and an asking price is not a completed sale. On the Mac side, the price ladder shows where a given listing falls and why the $625 floor is one listing rather than a market price.

Power supply search and other stores for the card and the mini

Power supply. A 3090 needs a supply that can feed it.

Find 750W ATX power supplies on Amazon. This opens search results, not a specific product. Nvidia rates a single-card 3090 system at 750W; match that rating and the connectors your exact board uses.

Other stores: eBay, r/hardwareswap and local pickup for the card, with GetPCParts tracking sold prices and ResalePrices tracking active asks; Apple’s own store, Apple Certified Refurbished, Swappa, PayMore and eBay for the mini, priced channel by channel in where to buy a used Mac mini.

Finish the decision at the used-card checks if the card wins, or the Mac model ladder if the mini does. A memory figure is not a compatibility guarantee, and we have not tested any of this hardware.

$2,000 to $3,500: 128GB of slow memory, or 48GB in two cards

Supports $2,000-$3,500: 128GB, capacity over speed · $2,000-$3,500, with a desktop that takes two cards · $2,000-$3,500 on a Mac: speed over capacity

What it is. Three different bets, and the budget covers all three.

Option	Memory, bandwidth	Price, dated	Largest class at about 4-bit	Speed	What you give up
GMKtec EVO-X2 or another Ryzen AI Max+ 395 (Strix Halo) box	128GB shared, 256GB/s	$3,499.99 for the EVO-X2 with 1TB at GMKtec’s store, listed as available Sept. 16; $27 a gigabyte, our arithmetic. The Framework Desktop’s $3,449 was listed out of stock the same day. Which route holds your model	gpt-oss-120b’s 65.4GB minimum, outright	4.5 t/s on a dense 70B and 33.7 t/s on gpt-oss-120b, measured on a Framework Desktop with the same chip (lhl, llama.cpp); Compute Market’s estimate for a 4-bit 70B is single-digit to low-double-digit t/s	Bandwidth: a third of a used M1 Ultra’s 800GB/s
Two used RTX 3090s	48GB total, 384-bit each	$2,064 at the Sept. 12 sold price, $2,674-$2,926 at the Sept. 15 asks, our arithmetic	Llama 3.3 70B’s 39.1GB minimum	gpt-oss-120b with 26 expert layers in system RAM: 10-17 t/s (LLM Garage, January 2026). For 70B, 16.29 t/s on Llama 3 70B at Q4_K_M, measured (XiongjieDai, May 2024 snapshot, llama.cpp); Compute Market estimates 14-16 t/s. Two cards or a 128GB box	700W of rated card power before the rest of the PC, at least a 1,000W supply, two slots spaced for both coolers and case room
A 36GB Mac Studio, new or used	36GB unified, 460GB/s or 410GB/s	M5 Max 36GB, $2,499 new (Apple, Aug. 25); used M4 Max 36GB, $2,750 median ask (Aug. 18-25)	Qwen 3.5 27B’s 15.5GB minimum, with working room	Estimated 21-27 t/s on the M5 Max and 19-24 on the used M4 Max, bandwidth arithmetic, not measurements	Capacity: no 70B, no 120B

What it runs, and what it cannot. The 128GB box is the only option here that holds a 120B-class model outright, and it is just under Qwen3-235B-A22B’s 131.3GB minimum, so the class above is out of reach at this budget in every column. It measured 4.5 t/s on a dense 70B on a same-chip Framework Desktop; two used 3090s measured 16.29 t/s on a 4-bit Llama 3 70B in a May 2024 run on different systems, and Compute Market’s estimate for the pair is 14-16 t/s. The 36GB Mac gives up both large classes and buys bandwidth instead.

A fourth bet ships Sept. 22: the M5 Pro Mac mini, $2,299 with 48GB and $2,699 with 64GB (Apple’s $1,699 24GB base plus its memory upgrades, Aug. 25); only the 64GB tier holds a 70B dense model, at an estimated 5-7 t/s, and what each M5 Pro tier holds sets all three tiers against the options in this table.

One rung above this budget, a used Mac Studio M1 Ultra holds the same 128GB at 800GB/s for a $5,249 median ask, $41 a gigabyte (Aug. 18-25). That is the price of turning capacity into speed, and it is about $1,749 above the $3,499.99 EVO-X2. Nvidia prices new flagship VRAM at $147 a gigabyte on a 32GB RTX 5090 and $167 on a 96GB RTX Pro 6000, both as reported in August, which is why capacity at this budget comes from used cards or unified memory rather than a new Nvidia card: the two used 3090s above work out to about $43 a gigabyte at that Sept. 12 sold price, by the used-GPU guide’s arithmetic.

We are not ranking individual Ryzen AI Max+ 395 boxes against each other here; this rung names the class and hands off.

Finish the decision at AI memory per dollar, which prices Strix Halo, DGX Spark, used Mac Ultras and Nvidia cards per gigabyte and per unit of bandwidth, with the constraint that decides listed first.

Where to buy each option: exact listings, searches and other stores

Where to buy.

See the GMKtec EVO-X2 with 128GB and 2TB on Amazon. This opens one exact product listing, whose title we checked Sept. 16, 2026; its seller, price, condition and stock have not been checked. It is the 2TB model, not the 1TB model priced at GMKtec’s store above.

Find Ryzen AI Max+ 395 mini PCs on Amazon. This opens search results, not a specific product. These machines are sold in several memory configurations, so confirm 128GB on the exact listing.

Find 1,000W ATX power supplies on Amazon. This opens search results, not a specific product, and it applies only to the two-card path.

Other stores: GMKtec’s own store, which listed the 128GB/1TB EVO-X2 at $3,499.99 and available Sept. 16; Framework’s own store, which listed the Desktop out of stock Sept. 15; GPUDojo’s RTX 3090 page, eBay and r/hardwareswap for used cards; Apple’s Mac Studio store and Apple Certified Refurbished for the Mac.

What no budget here buys

What no budget here buys: the 235B and 671B classes, frontier quality and certainty about speed

Supports 235B-class models, or frontier quality

The 235B and 671B classes. Those need 131.3GB and 375GB at about 4-bit, which is Mac Studio territory at $9,999 and $17,448 median asks (Aug. 18-25). The Mac model ladder prices those tiers with measured speeds, and above 512GB no single machine holds the model at all: the cluster math covers what stacking Studios does and does not buy.

Frontier-model quality. The open models on every rung here are a tier below the $200-a-month services. The subscription comparison weighs local capacity against what those plans cost over the same period, which is the honest alternative to rung four and sometimes to rung three.

Certainty about speed at depth. Every measured figure on this page is a short benchmark on one contributor’s machine. Real prompts are longer, and the one measured example we have of that gap cut generation to a quarter of the benchmark rate.

This page keeps no prices of its own. Each figure lives on the page that maintains it, dated there; when one of those pages updates, follow the link for the current version.