Best budget GPU for local LLMs: 12 GB, 16 GB or 24 GB?

Building in a slim workstation or a case with half-height slots? Our RTX 2000 Ada vs RTX A2000 low-profile GPU guide compares the 16GB and 12GB PNY cards, their brackets and slot-powered fit.

Which GPU should you buy for local LLMs?

Start with VRAM and the software you plan to run, not the biggest number on the box.

• Best straightforward 16 GB pick: ASUS Dual RTX 5060 Ti OC, with the CUDA route on Windows or Linux. The verified white OC edition is the finish alternative, with the same capacity and connector layout.
• 16 GB Linux ROCm alternative: ASUS Dual RX 9060 XT. Ollama explicitly lists it for Linux ROCm; its Windows ROCm list differs.
• Small-model pick: MSI RTX 3060 VENTUS 2X 12G OC for a smaller quantized-model target that fits 12 GB.
• Larger single-card pool: ZOTAC RTX 3090 Trinity OC 24 GB, Renewed, when your workload needs more than 16 GB and you accept renewed hardware.

Specs and software support come from makers and project docs; the explicitly named 3060 Ti/2060 results below were measured on our bench.

Compare the exact cards

Card / selected condition	VRAM	Memory bus / theoretical bandwidth	Vendor board-power figure	PCIe power inputs	Length / slots or thickness
MSI RTX 3060 VENTUS 2X 12G OC — New	12 GB GDDR6	192-bit / 360 GB/s	170 W	1 × 8-pin	235 mm / 42 mm thick; slot rating not listed
ASUS Dual RTX 5060 Ti OC — New	16 GB GDDR7	128-bit / 448 GB/s	Exact ASUS limit not listed; NVIDIA reference: 180 W	1 × 8-pin	229 mm / 2.5 slots, 50 mm thick
ASUS Dual RX 9060 XT — New	16 GB GDDR6	128-bit / 320 GB/s	Exact ASUS board limit not listed; 550 W whole-system PSU recommended	1 × 8-pin	202 mm / 2.5 slots, 49 mm thick
ZOTAC RTX 3090 Trinity OC — Renewed	24 GB GDDR6X	384-bit / 936 GB/s	350 W maximum	2 × 8-pin	317.8 mm / 2.5 slots, 58 mm thick

The black and white ASUS 5060 Ti editions share the listed 16 GB memory, 28 Gbit/s rate, 128-bit bus, dimensions and power input, per their exact black and white specifications. For Radeon, use the ASUS DUAL-RX9060XT-16G sheet.

Sources: MSI exact-model datasheet, ASUS exact-model specifications, ZOTAC ZT-A30900J-10P brochure, and NVIDIA RTX 5060 Ti reference specifications.

Compare VRAM capacity with the exact model, context and concurrency requirement. The table is a specification comparison, not a measured speed ranking.

The bandwidth figures are calculated ceilings: vendor effective memory rates of 15, 28, 20 and 19.5 Gbit/s, in table order, multiplied by bus width and divided by eight. They are decimal GB/s, not measured throughput, PCIe bandwidth or tokens per second. A narrower bus with faster memory can have a higher theoretical figure; bus width alone does not rank these cards.

CUDA or ROCm: choose the software path before the card

For the straightforward Ollama pick on Windows or Linux, choose the RTX 5060 Ti 16 GB. NVIDIA uses the CUDA route, and Ollama lists RTX 3060, 3090 and 5060 Ti support. Install a current NVIDIA driver that supports your exact card and update Ollama when moving to RTX 50-series hardware.

The RX 9060 XT 16 GB is our Linux ROCm alternative. Ollama’s hardware-support page lists it in the Linux table and calls for the ROCm 7 driver. Pair the card with a supported release and operating system from AMD’s Radeon Linux support matrix. A Radeon badge and enough VRAM are only half the shopping decision: the GPU also needs a supported backend.

Your software plan	Pick from this guide	Why
Ollama on Windows, minimal backend decisions	RTX 5060 Ti 16 GB	Listed NVIDIA family, CUDA route and more model room than 12 GB
Ollama on a supported Linux ROCm setup	RX 9060 XT 16 GB	Specifically listed for Linux ROCm; same capacity tier as the NVIDIA pick
Ollama on Windows with RX 9060 XT	On Windows, choose the NVIDIA CUDA pick; explore Vulkan separately if you want AMD	RX 9060 XT is absent from Ollama’s Windows ROCm table
Building llama.cpp yourself	Match the build to the card	NVIDIA CUDA, AMD HIP/ROCm and Vulkan are separate backend choices

That Windows distinction matters: Linux ROCm support does not automatically carry over to Windows ROCm. Ollama also documents Vulkan support on Windows and Linux when that backend is installed, giving AMD users another route. Decide which route you will use before buying, then verify the actual loaded backend and model placement after installation.

If you use llama.cpp directly, its build documentation separates GGML_CUDA for NVIDIA, GGML_HIP for AMD through ROCm, and GGML_VULKAN. Pick the matching build and drivers rather than copying a CUDA command onto an AMD machine. These are software-support choices; the hardware table above supplies no tokens-per-second ranking between them.

Choose the model before choosing the card

Use the exact model tag and quantization, then budget for context, concurrent requests and other loaded models. The Ollama context and VRAM guide shows what to record and how to inspect actual placement. A model download is not the entire runtime allocation.

The following are buying starting points, with the final fit check made at the settings you intend to use:

Intended workload	Starting pick	Capacity reason	Check before keeping it
7B/8B-class quantized chat, limited concurrency	MSI 12 GB	12 GB for the specified smaller-model target	Exact model, context and full GPU placement
A 14B quantized model, or more room around a smaller model	ASUS 5060 Ti 16 GB, or RX 9060 XT 16 GB on Linux ROCm	16 GB for more runtime headroom	Context, cache and simultaneous requests together
A 32B Q4 target whose allocation exceeds 16 GB	ZOTAC 24 GB Renewed	Larger single-card capacity	Actual allocation fits 24 GB and renewed terms are acceptable
Current card already completes the task satisfactorily	Keep it	Avoid an upgrade that solves no identified problem	Repeat the required workload before spending

VRAM versus model size: do the weights fit first?

Ollama lists qwen2.5:7b-instruct-q4_K_M at 4.7 GB, 14b-instruct-q4_K_M at 9.0 GB and 32b-instruct-q4_K_M at 20 GB, checked 2026-10-23. Use these exact quantizations, not parameter count alone.

For the worksheet, convert those rounded decimal GB downloads to GiB, then subtract them from assumed 12, 16 and 24 GiB capacity budgets. One decimal GB is 1,000,000,000 bytes; one GiB is 1,073,741,824 bytes. This keeps the arithmetic in one unit. The results describe space after model weights only, before KV cache, compute buffers, display/driver use and other loaded work.

The rounded downloads convert to 4.38 GiB (7B), 8.38 GiB (14B) and 18.63 GiB (32B).

All entries below are GiB. The last three columns show space after weights; a minus sign means the weights already exceed that assumed capacity budget.

Q4_K_M model	12 GiB	16 GiB	24 GiB
7B	7.62	11.62	19.62
14B	3.62	7.62	15.62
32B	−6.63	−2.63	5.37

That makes 16 GB the comfortable starting tier for a 14B Q4 buying decision: you gain room for the runtime instead of spending nearly all the budget on weights. For the named 32B package, 16 GiB is already short before cache enters the picture; 24 GB is the single-card tier to investigate. Longer context and parallel requests consume more memory, so finish the decision with ollama ps at your intended settings. Our context and VRAM guide walks through that check.

Small-model pick: MSI RTX 3060 12 GB

Buy this MSI when the smaller-model workload is the goal and the complete build fits its requirements. There is no reason to buy the renewed 3090 for a task this card can already satisfy.

Match RTX 3060 VENTUS 2X 12G OC exactly. A 3060 Ti is a different GPU; an unspecified memory variant is not an adequate listing description. The 12 GB in this recommendation is a vendor specification, not a measurement from our recorded Ti build.

If a 14B model is the main reason for buying, prefer the ASUS’s extra room when the budget allows. Leave room for the context and simultaneous requests you actually want to run. Conversely, do not pay for more memory simply because the newer model name sounds better.

More room for a 14B target: ASUS RTX 5060 Ti 16 GB

Choose the ASUS for 16 GB of VRAM when the intended model, context and concurrency need more room than the 12 GB option. This is a capacity recommendation, not a measured speed ranking.

Check DUAL-RTX5060TI-O16G, including the 16 GB designation. ASUS lists a single 8-pin power input and a 2.5-slot cooler. Plan the space around the card as well as its length: the cable, neighbouring expansion cards and airflow all need room.

ASUS’s page gives a whole-system PSU recommendation but omits an exact board-power limit. The table therefore labels 180 W as NVIDIA’s reference figure. Do not substitute a recommended PSU wattage for GPU consumption or assume a software cap proves the full system is adequately powered.

AMD 16 GB alternative: ASUS RX 9060 XT

Choose the ASUS Dual RX 9060 XT 16 GB when you want a 16 GB card for the supported Linux ROCm route. The capacity target is the same smaller-model/14B workload as the NVIDIA pick. This is a software-fit alternative, not a claim that either card wins an inference benchmark.

The exact DUAL-RX9060XT-16G is 202 mm long and 49 mm thick, with a 2.5-slot design and one 8-pin power input. ASUS recommends a 550 W whole-system PSU. Its reviewed page omits an exact board-power limit; use the full-system worksheet in our PSU buying guide rather than treating a generic GPU-family power figure as this card’s limit.

For Windows Ollama with the simplest documented shortlist, stick with the 5060 Ti. For Linux ROCm, the RX 9060 XT earns its place because the project explicitly lists the card. Either way, choose the model and backend before you choose the cooler colour.

Should you buy an RTX 5070 12 GB instead?

For this VRAM-first purchase, the useful upgrade from 12 GB is more memory, not simply a higher model number. NVIDIA lists RTX 5070 at 12 GB; it still has the same nominal capacity tier as the 3060 12 GB. Buy the 16 GB pick when a larger model or context is the reason for upgrading; compare speed separately using benchmarks for your exact workload.

Buy 24 GB only for a workload that needs it

Choose the 24 GB card when the actual runtime allocation needs that larger single-card memory pool. For a smaller workload, use the 12 GB or 16 GB option that fits.

Use the 32B worksheet as the starting point, then reserve memory for your chosen context and parallel requests. Inspect actual allocation before deciding that 24 GB is enough for the whole job.

The selected offer is Amazon Renewed, not New. Confirm the condition grade and current seller’s return and guarantee terms. Do not assume original accessories or a new-card manufacturer warranty. For a separate used-card route, follow our Buying used enterprise gear safely guide to check identity, condition and return terms before paying.

What our measured cards actually show

Our September 25, 2026 bench record covers an RTX 3060 Ti 8 GB and RTX 2060 6 GB. Each empty card ran alone with Ollama 0.34, Q4_K_M models, flash attention and q8_0 KV cache. After warm-up, each row averaged three 512-token generation runs, seed 1 and temperature 0, at respective 150 W and 125 W caps. Context was not separately recorded.

Recorded model	RTX 3060 Ti 8 GB	RTX 2060 6 GB
qwen2.5:7b	75.8 tok/s; 100% on GPU	57.7 tok/s; 100% on GPU
qwen2.5:14b	14.5 tok/s; 71% on GPU	8.6 tok/s; 48% on GPU

“On GPU” describes model placement, not utilisation. Both 14B results include CPU work; they do not isolate a GPU-only speed gap. These observations explain why residency belongs beside speed. Use them to understand why offload matters; use the vendor specifications and software support above to choose the buying cards. The full measured comparison preserves the other workloads and method details.

Check fit, then verify during the return window

Match the exact driver, operating system and backend described above, especially when moving an existing installation to a newer card.

Budget the whole system and the exact card’s power inputs. The PSU buying guide explains separate GPU cables and full-system allowances. ASUS recommends a 550 W whole-system PSU for both named 5060 Ti editions and the RX 9060 XT. That is a system recommendation, not GPU consumption or a two-card power budget. Each uses one 8-pin input; give separate cards their own PSU cables. Physical fit includes length, thickness, cable clearance and cooling with the enclosure closed.

On arrival, verify card identity and available VRAM, then run the intended model at an explicit context and concurrency. Inspect ollama ps for actual placement and context, and complete the real task before judging the purchase. Record versions and settings so a repeat means something.

Need more memory than one card offers? Our local-LLM hardware guide compares a 16–24GB NVIDIA desktop with a 128GB Ryzen AI Max+ 395 mini PC, with Q4/Q8 model sizes and bandwidth-based planning.