The verdict
3.2 /5
Superseded by a card with 56% more bandwidth for 15 W more, and worth buying only when the used price gap is real. It fits what every 16 GB card fits and takes longer about it. Check your PCIe slot before planning to offload — this is the one card here where that matters.
Scored against our methodology
Buy it if
Getting 16 GB of CUDA memory into an older or low-power machine cheaply, when it is meaningfully less expensive than an RTX 5060 Ti and speed is not what you are optimising for.
Skip it if
Anyone who will sit and watch it generate; anyone planning to offload models past 16 GB, where the halved PCIe lanes bite; or anyone for whom the price gap to a 5060 Ti is small.
Strengths
- 16 GB reaches exactly the same models as far more expensive cards
- 165 W and a 550 W supply — the least demanding card on this site
- Usually works with the power supply already in an older machine
- Holds up well for image generation, where the gap to its replacement is only 6%
- Cool and quiet under sustained load
- Cheaper than its replacement on the used market, which is the whole case for it
Trade-offs
- 288 GB/s is the slowest here — 56% behind its direct replacement
- PCIe 4.0 ×8, which becomes a real constraint when offloading, especially on a PCIe 3.0 board
- No FP4 support, which the newer generation has
- 16 GB is a hard wall at 14B; 32B models do not fit
- Superseded by a card that is better in almost every respect for 15 W more
- A used RTX 3090 offers more than three times the bandwidth and half again the capacity
This is the slowest card reviewed on this site, and it has been superseded by a direct replacement that is better in almost every respect. There is exactly one circumstance in which it is the right purchase, and the review will get to it — but it is narrow, and it is entirely about price.
It is also the one card here where a piece of general advice this site gives elsewhere does not apply, which is the most useful thing on this page and the reason it is worth reading before buying one second-hand.
| Identity | |
|---|---|
| Manufacturer | NVIDIA |
| Product family | GeForce RTX 40 Series |
| Model | RTX 4060 Ti 16GB |
| Form factor | Graphics card |
| Release year | 2023 |
| Price class | Budget (under $500) |
| Compute | |
| GPU | AD106 Ada Lovelace, 4,352 CUDA cores |
| GPU architecture | Ada Lovelace (AD106) |
| VRAM (GB) | 16 |
| VRAM type | GDDR6, 128-bit |
| Memory bandwidth (GB/s) | 288 |
| Compute capability note | PCIe 4.0 ×8 only — half the lanes of a full-width card, which matters when offloading |
| Connectivity & physical | |
| Power draw | 165 W total board power; 550 W system PSU recommended |
| Workload suitability | |
| Local LLMs | Workable with caveats |
| Ollama | Workable with caveats |
| Stable Diffusion / ComfyUI | Good |
| Homelab | Excellent |
| Developer workstation | Workable with caveats |
| Largest comfortable model | 14B at Q4, slowly |
Against its own replacement
The RTX 5060 Ti 16GB is the same idea, one generation on, and the comparison is not close.
| RTX 4060 Ti 16GB | RTX 5060 Ti 16GB | Difference | |
|---|---|---|---|
| VRAM | 16 GB GDDR6 | 16 GB GDDR7 | Same capacity |
| Memory bandwidth | 288 GB/s | 448 GB/s | +56% |
| CUDA cores | 4,352 | 4,608 | +6% |
| Board power | 165 W | 180 W | +15 W |
| Low-precision formats | FP8 | FP8 and FP4 | — |
56% more bandwidth for 15 W more. Since generation speed tracks bandwidth, that is close to 56% more tokens per second on identical models — and the newer card also gains FP4 support, which the older Ada generation does not have.
The 4060 Ti’s only structural advantage is those 15 W, and 15 W is not an argument. Its real advantage is that it is older, so on the used market it should cost less. That is the entire case, and whether it holds depends on a price you will have to check yourself.
The exception to a rule we give elsewhere
The machine you already own tells readers that an old PCIe generation is almost never the problem, and not to replace a working motherboard over it. That advice is correct, and this is the card it does not cover.
This one runs at PCIe 4.0 ×8 — half the lanes of a full-width card. That is fine in the ordinary case, because once a model is resident in VRAM the card barely uses the bus at all. The general rule holds.
It stops holding in the specific case this card invites. Someone buying a slow 16 GB card is more likely than most to try running a model larger than 16 GB by offloading layers to system memory — and offloading pushes weights across that link continuously, at which point half the lanes is half the bandwidth for the traffic that matters.
It compounds on an older machine, which is precisely where this card is likely to end up:
| Slot | Effective for ×8 |
|---|---|
| PCIe 4.0 ×16 slot | ~16 GB/s |
| PCIe 3.0 ×16 slot | ~8 GB/s |
On a PCIe 3.0 board the card negotiates ×8 at Gen 3 speeds, and offloading becomes genuinely painful rather than merely slow. If your plan involves running models bigger than the card, check your slot generation first. If your plan is to stay inside 16 GB, ignore all of this — the general rule applies and the bus is irrelevant.
What it actually runs
Same capacity as every 16 GB card on this site, so the same ceiling.
| Model class | At Q4_K_M | On this card |
|---|---|---|
| 8B | ~5 GB | Fine, with context to spare |
| 14B | ~8 GB | Around reading speed |
| 27–32B | ~16–19 GB | Does not fit |
| 70B | ~42 GB | No |
The arithmetic for a 14B model: 288 GB/s divided by roughly 8 GB of weights gives a ceiling near 36 tokens per second, and real throughput sits below that. That is around conversational reading pace — usable, and the slowest usable on this site. The 5060 Ti reaches about 56 by the same arithmetic, and the 5070 Ti about 110.
The product record for this card says “14B at Q4, slowly”, and that is the honest summary. It fits what the newer cards fit; it takes longer about it.
Where it is still fine
Image generation is the workload where it holds up best, oddly enough. Diffusion is compute-bound rather than bandwidth-bound, and the shader gap to the 5060 Ti is only 6% — far smaller than the 56% bandwidth gap. SDXL is comfortable in 16 GB. It is slow in absolute terms because the chip is small, but it is not disproportionately slow the way it is for language models.
Low-power always-on use. 165 W and a 550 W supply is the least demanding combination on this site, and for a machine whose job is to be available rather than fast, that has real value — the reasoning is in the always-on server.
Getting 16 GB into an old machine cheaply. If the alternative is not running models at all, this card runs them.
Buying one
Used only, and the checks are lighter than for a 3090 because there is less to go wrong: a 165 W card has not been stressed the way a 350 W one has, and this generation’s memory does not have the GDDR6X heat problem.
Still worth doing: ask what it did, confirm the fans spin freely, and confirm the price gap to a 5060 Ti is real. That is the only thing making this card the right answer, so it is the thing to verify. If the two are close in price, there is no argument for this one.
Also confirm your slot, per the section above, if offloading is part of the plan.
On the score
This card totals 3.2, the lowest of the seven GPUs reviewed here, and that is the correct place for it.
An earlier version of this page said something different, and it is worth recording why. Under a flat average of eight criteria this card scored above the RTX 3090 — a far stronger card for local AI — because three of those criteria reward a small, cool, 165 W machine and this is one. That was the scale measuring the wrong thing, and this review is what surfaced it.
The criteria are now weighted, with AI performance carrying four times an ordinary criterion and value twice. The methodology page has the table. Under that scale this card sits last, the RTX 3090 sits fourth, and the ordering matches what the reviews actually say about the cards.
The rows that decide it here are 2.4 for AI performance and 3.2 for value — the two things a reader of this site is most likely to care about, and the two where this card is weakest. Its 4.6 for thermals is real and it is not the reason to buy a graphics card.
Who should buy one
Buy it if it is meaningfully cheaper than an RTX 5060 Ti 16GB, you need 16 GB, and speed is not what you are optimising for. That is the whole case, and it stands or falls on the price gap.
Buy an RTX 5060 Ti 16GB instead in essentially every other situation. 56% more bandwidth for 15 W more is not a close call, and it is the card this one was replaced by.
Buy a used RTX 3090 instead if you can supply 350 W and want capability rather than efficiency. 24 GB and 936 GB/s is a different tier — more than three times the bandwidth, and 32B models this card cannot load.
Do not buy it expecting to offload past 16 GB, particularly on an older motherboard. Half the PCIe lanes is the wrong hardware for that plan.
Frequently asked questions
Is the RTX 4060 Ti 16GB good for local AI?
It is adequate rather than good, and only worth buying on price. 16 GB runs 14B-class models at around reading speed — 288 GB/s divided by roughly 8 GB of weights gives a ceiling near 36 tokens per second. It reaches exactly the same models as every other 16 GB card and takes longer about it. If it is meaningfully cheaper than an RTX 5060 Ti 16GB, that is the case for it; if it is not, there is no case.
RTX 4060 Ti 16GB or RTX 5060 Ti 16GB?
The 5060 Ti, unless the price gap is substantial. Same 16 GB, but 448 GB/s against 288 — 56% more bandwidth, which translates almost directly into tokens per second — for 15 W more. It also adds FP4 support, which Ada does not have. The older card’s only structural advantage is those 15 W, and 15 W is not an argument.
Does the PCIe ×8 limitation matter?
Only if you offload. Once a model is resident in VRAM the card barely uses the bus, so half the lanes changes nothing and the general advice about old PCIe generations holds. It matters when you run a model larger than 16 GB by pushing layers into system memory, because that traffic crosses the link continuously — and on a PCIe 3.0 board, ×8 at Gen 3 speeds makes offloading genuinely painful. Check your slot before planning around it.
Can it run a 32B model?
No. A 32B model at Q4_K_M needs roughly 19 GB for weights alone, before any context, so it does not fit in 16 GB on this card or on any other. Offloading makes it run, slowly, and this is the card least suited to that approach because of the halved PCIe lanes. Reaching that tier means 24 GB — a used 3090, a 4090 or a 5090.
Is it any good for Stable Diffusion?
This is where it holds up best. Diffusion is compute-bound rather than bandwidth-bound, and the shader gap to the 5060 Ti is only 6% against a 56% bandwidth gap — so the deficit that hurts language models barely applies. SDXL fits comfortably in 16 GB. It is slow in absolute terms because the chip is small, but not disproportionately so.
How does it compare to the RTX 3090 on score?
It sits last of the seven cards reviewed here at 3.2, against the 3090’s 3.7, and the gap understates the difference in capability — 2.4 against 4.0 for AI performance, and 3.2 against 4.5 for value. That ordering is recent: under the flat average this site used previously, this card scored higher than the 3090, which was the scale rewarding a small cool 165 W card rather than measuring what readers come here for. The criteria are now weighted, and this review is what surfaced the problem.
What power supply does it need?
A 550 W unit is the usual recommendation for 165 W of board power, which makes this the least demanding card on the site — it will usually work with whatever is already in an older machine, and that is a genuine part of its appeal. As always the ATX 3.1 standard matters alongside the wattage, though transient behaviour is far less of a concern at this power level than on a 350 W or 575 W card.
Did AI Gear Stack test this card?
No. This assessment is drawn from manufacturer specifications and published architectural detail, as stated at the top of the page. Throughput figures are calculated from the memory bandwidth specification rather than measured. Acoustics, sustained thermal behaviour and the condition of any particular second-hand card are the questions this method cannot answer, and we say so rather than guessing.