Best Budget Local AI PC: What Each Price Tier Buys
The best budget local AI PC at four price tiers, from our 16 GB starter builds to $10k+ systems, with the models each one fits and estimated speeds.
Updated · By RigPerDollar
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The best budget local AI PC for most people is the one used RTX 3090 build at $2,585.58. It has 24 GB of VRAM and fits 61 of the 87 models we track. If you must stay under $2,000, get the RTX 5060 Ti 16 GB build at $1,951.57. The faster RX 9070 build is $2,101.57 at current prices, over that line. If you need 70B models, the dual RTX 3090 build at $4,557.95 is the cheapest box we price that runs them. All build prices are full parts lists from our builds page, from listings dated Oct 19–26, 2026, before tax and shipping.
Two terms come up below. VRAM is the memory on the graphics card. The whole model file has to fit in it, plus some room for the chat history. tok/s (tokens per second) is how fast the model writes its reply. A token is a word or a piece of a word. Every speed here is our estimate from memory bandwidth and model size, checked against measured llama.cpp runs. See how we estimate speed. Real speeds can land about 15% either way.
The four tiers at a glance
| Tier | Box | Price | Memory for models | Models that fit (of 87) |
|---|---|---|---|---|
| Under $2,000 | RTX 5060 Ti 16 GB build | $1,951.57 | 16 GB | 39 |
| $2,000 to $4,000 | RX 9070 build | $2,101.57 | 16 GB | 39 |
| $2,000 to $4,000 | Used RTX 3090 build | $2,585.58 | 24 GB | 61 |
| $2,000 to $4,000 | Dual RX 9070 build | $3,589.93 | 32 GB | 63 |
| $4,000 to $10,000 | Dual used RTX 3090 build | $4,557.95 | 48 GB | 65 |
| $4,000 to $10,000 | Framework Desktop, 192 GB | from $6,799 (DIY) | 192 GB | 82 |
| $10,000 and up | No priced box yet |
Build prices are from listings dated Oct 19–26, 2026 and exclude tax and shipping. The Framework price is the frame.work DIY Edition starting price seen Oct 16, 2026. It leaves out storage and an operating system, so a working system costs more.
Speed on the same models
These are the estimated speeds for four reference models at Q4_K_M, the common 4-bit file size. A blank cell means the model does not fit.
| Box | Gemma 4 12B | Qwen3.8-27B | Llama 3.3 70B | gpt-oss-120b |
|---|---|---|---|---|
| RX 9070 (16 GB) | 69 | |||
| RTX 5060 Ti 16 GB | 52 | |||
| Used RTX 3090 (24 GB) | 102 | 40 | ||
| Dual RX 9070 (32 GB) | 69 | 26 | ||
| Dual used RTX 3090 (48 GB) | 102 | 40 | 19 | |
| Framework Desktop (192 GB) | 30 | 12 | 5 | 65 |
A second card adds memory and leaves speed where it was. The dual 3090 runs Qwen3.8-27B at the same 40 tok/s as one 3090, because the cards take turns on each token. The Framework fits far more than any GPU build, but it writes dense models much slower. Its memory is about 273 GB/s, against 936 GB/s on an RTX 3090.
Under $2,000: a 16 GB card
At current prices the RTX 5060 Ti 16 GB build at $1,951.57 is the build we price under $2,000. The RX 9070 build is $2,101.57, $150 more and over budget, and fits the same 39 models. The RX 9070 has more bandwidth (640 GB/s vs 448 GB/s), so it is faster on most of them. Gemma 4 12B runs at an estimated 69 tok/s on the RX 9070 and 52 tok/s on the 5060 Ti. If you can go a little over $2,000, the RX 9070 build buys that speed. Stay with the NVIDIA build if the budget is firm or a tool you rely on needs CUDA.
16 GB tops out around 24B dense models. Mistral Small 3.2 24B fits at about 34 tok/s on the RX 9070. Small mixture of experts (MoE) models are where 16 GB shines. They only read part of the model for each token, so JetBrains Mellum2.1 12B-A2.5B runs at an estimated 173 tok/s and gpt-oss-20b at 154 tok/s. Qwen3.8-27B does not fit. Its 17.44 GB file is larger than the card.
For a part by part comparison of the two 16 GB boxes, see best PC for local LLMs under $2,000.
$2,000 to $4,000: one used RTX 3090
This is the best value tier. The used RTX 3090 build costs $2,585.58, about $484 more than the RX 9070 box. For that you get 8 GB more memory, 22 more models that fit, and about 1.5 times the speed on models both can run. Qwen3.8-27B fits at an estimated 40 tok/s, and 30B class MoE models such as Qwen3.6-35B-A3B run near 157 tok/s.
The dual RX 9070 build at $3,589.93 has 32 GB, but it fits only 2 more models than the 3090 box and runs them slower (Qwen3.8-27B at 26 tok/s). It also needs a board with room for two cards. Get it only if you want to buy new cards instead of a used one.
A Mac Studio also starts in this tier. Apple listed the M5 Max with 36 GB from $2,499 on Oct 16, 2026. macOS lets the GPU use only part of that memory by default, about 24.1 GB by our estimate. That puts it level with the 3090 box at 61 models, but slower: about 21 tok/s on Qwen3.8-27B. See the Mac Studio M5 Max 36 GB page for details.
$4,000 to $10,000: 48 GB or 192 GB
For 70B models, buy the dual used RTX 3090 build at $4,557.95. Llama 3.3 70B fits at an estimated 19 tok/s, and everything the single 3090 runs stays just as fast. Two triple slot cards need a case and board with space, so check clearance before you order.
The Framework Desktop starts at $6,799 for the DIY Edition (Oct 16, 2026). Its 192 GB fits 82 of 87 models, including gpt-oss-120b at an estimated 65 tok/s. No GPU build we price can load that model. The trade is speed on dense models: Llama 3.3 70B drops to about 5 tok/s. Buy it for large MoE models, and buy the dual 3090 for dense 27B to 70B models. Our Strix Halo vs GPU guide goes deeper on that choice.
$10,000 and up: no priced box yet
We do not have a dated full price for any box in this tier, so we will not guess one. The builds table stops at $4,557.95. We have no price yet for the Mac Studio M5 Ultra, the NVIDIA DGX Spark or a 128 GB Strix Halo mini PC.
We do have speed estimates for them. You can check any model on each one in My Rig:
- Mac Studio M5 Ultra, 96 GB: about 72 GB usable by the GPU, 71 models fit. Llama 3.3 70B at about 24 tok/s and gpt-oss-120b at about 130 tok/s.
- NVIDIA DGX Spark, 128 GB: 78 models fit. gpt-oss-120b at about 76 tok/s, Llama 3.3 70B at about 6 tok/s.
- Ryzen AI Max+ 395, 128 GB: 78 models fit. gpt-oss-120b at about 61 tok/s, Llama 3.3 70B at about 5 tok/s.
Of the three, the Mac Studio M5 Ultra has by far the most bandwidth (1,200 GB/s), which is why it is the only one that runs 70B dense models at a usable speed.
How to pick
- Find the largest model you want to run on its model page and note the file size.
- Add about 1.5 GB for the runtime and a short chat history. That is the memory you need.
- Take the cheapest tier above with that much memory.
If you already own a GPU or a Mac, enter it in My Rig first. It lists every model that fits and its estimated speed, so you may not need a new box at all.