Which 16 GB card for AI for ~PLN 2,000 (LLM + Stable Diffusion)

Short answer: for about PLN 2,000 you have a real choice between "a lot of VRAM for less, but with compromises" (a used data-centre V100 16GB / P100 16GB / A2 16GB card) and "convenient and trouble-free" (a consumer RTX 3060 12GB or 4060 Ti 16GB). If the card is going to sit in a server/PC 24/7 and what matters to you is the maximum memory for the model, take a Tesla. If this is your first time with local AI, you want to plug in a monitor and not fight with drivers, a consumer card will be less painful. And yes, we sell Teslas, so you will find the full disclaimer at the end.

I am writing this from the perspective of someone who actually builds home and business machines for local models, not from a manufacturer's table. So let me clear up one misconception straight away: "16 GB for PLN 2,000" sounds like an obvious case for a used Tesla, but for some people it will be a bad purchase. Below I lay it out honestly: what you get, what you lose and when each choice makes sense.

Why 16 GB and why PLN 2,000

Because that is the real entry threshold for sensible local AI. Below 12 GB things get tight: Stable Diffusion XL still runs on 8 GB but with less room for extras, while quantised Flux and 13B-class LLMs start to choke. 16 GB of VRAM is comfortable: SDXL without constantly freeing memory, and LLMs of around 13B in Q4 are much more comfortable than on 12 GB: the extra memory gives more headroom for the KV cache and a longer context. And PLN 2,000 is the amount at which a new 16 GB card is either a compromise (the RTX 5060 Ti 16GB or the older 4060 Ti 16GB, both on a 128-bit bus) or no longer fits the budget (an RTX 5070 Ti and above). The used data-centre market opens doors here that new hardware at this price does not.

A note on prices: specific amounts change, so treat "~PLN 2,000" as an indicative ceiling, and check the current prices of consumer cards and of our Teslas before deciding.

Comparison of the real options

Card	VRAM	FP16 / Tensor Cores	Power draw	Display output	Ease of driver setup
V100 16GB	16 GB HBM2	Very good (Tensor Cores, Volta)	~250 W	None	Harder (Tesla, passive)
P100 16GB	16 GB HBM2	Good (FP16 2:1)	~250 W	None	Harder (Tesla, passive)
A2 16GB	16 GB GDDR6	Good (Tensor Cores, Ampere), limited bandwidth	40–60 W (configurable)	None	Medium (newer Ampere)
RTX 3060 12GB	12 GB GDDR6	Good (Tensor Cores, Ampere)	~170 W	HDMI/DP	Easy (consumer driver)
RTX 4060 Ti 16GB	16 GB GDDR6	Good (Tensor Cores, Ada), narrow bus	~165 W	HDMI/DP	Easy (consumer driver)
RTX 5060 Ti 16GB	16 GB GDDR7	Good (Tensor Cores, Blackwell), 128-bit bus (448 GB/s)	~180 W	HDMI/DP	Easy (consumer driver)

Measured on our own units (bench: 2× Xeon E5-2697 v3, Ubuntu 24.04, driver 580.173.02, system CUDA Toolkit 12.0, PCIe Gen3, full power limit). LLM: llama.cpp (ggml 0.19.0, commit 6ad4ab0), llama-bench -ngl 99 -p 512 -n 128 -r 3, file Bielik-11B-v2.3-Instruct.Q4_K_M.gguf. SDXL: diffusers 0.39 + PyTorch 2.5.1+cu121 (a wheel with its own CUDA 12.1 runtime), SDXL base 1.0, 1024², 30 steps, Euler a sampler, batch 1 (warm-up + 3 images). These are results of this particular pipeline, not a universal ceiling for the card; we will share the per-run raw output on request:

(Note: the measurements below cover the accelerators from our server lab. We test the consumer RTX 3060 and 4060 Ti cards on a separate platform and plan to add a direct comparison of them in a separate piece.)

Card	LLM prefill (tok/s)	LLM generation (tok/s)	SDXL (images/min)
V100 16GB	1963	77.7	7.05
P100 16GB	421	27.3	1.06
A2 16GB	370	17.6	1.62

The clear conclusion: the V100 wins decisively in our measurements. In the SDXL test it reached 7.05 images/min against 1.06 for the P100: about 6.7× more. This lead is a combination of several factors at once: the newer Volta architecture with Tensor Cores, the higher HBM2 bandwidth and the better fit of modern AI computations to this architecture; the benchmark does not separate them, so we do not attribute the whole difference to one thing. The A2 reached 370 tok/s of prefill, i.e. ~88% of the P100's result, at a configurable TDP of 40–60 W against the P100's 250 W: a very good result at low power draw, although it is not a direct measurement of energy efficiency. Its LLM generation is clearly weaker than its prefill: the prefill phase in llama.cpp relies mainly on raw compute power, whereas token-by-token generation is usually much more sensitive to memory bandwidth than prefill. In the A2's case, the likely limitation is its much lower memory bandwidth (GDDR6 ~200 GB/s against HBM2), but the benchmark does not separate the effect of bandwidth from the other differences in architecture and implementation. The P100 lands in the middle: generation is faster than on the A2 in our measurements, although its 732 GB/s memory bandwidth alone might suggest a larger lead. One possible limitation is the lack of native __dp4a support at compute capability 6.0: this llama.cpp version emulates those operations in quantised dot products. The benchmark does not establish how much this affected the result. It is the slowest in our SDXL tests (Pascal without Tensor Cores). The figures are our measurements, not numbers pulled out of thin air.

What you get with a Tesla, and what you pay for it

The advantages of a Tesla are concrete. The V100 16GB is Volta with Tensor Cores and fast HBM2 memory. It outperforms the A2 and P100 in our LLM inference and SDXL measurements; we did not directly measure its advantage over consumer cards or its training performance here. Its 900 GB/s HBM2 bandwidth is more than three times that of the A2 or RTX 4060 Ti 16GB. The P100 16GB has strong FP16 at a 2:1 rate and solid FP64 (4.7 TFLOPS) on top, at a much lower price than the V100: if you do scientific computing (simulations, HPC) alongside AI on a tight budget, that is its strength (the V100 is faster still in FP64, at 7 TFLOPS). The A2 16GB has the newest architecture of the three (Ampere), draws a mere 40–60 W (configurable TDP) and is low-profile: it is very well suited to a small server with thermal and power constraints that runs around the clock and does not push up the electricity bill.

But now, honestly, about the drawbacks, and I am not hiding them in the small print:

• No display output at all. Tesla cards have no HDMI or DisplayPort. They are compute accelerators, not graphics cards. You need a second card (or the iGPU in the processor) to connect a monitor. For a headless server this is irrelevant. For someone who wants one card to both game and compute, it is a dealbreaker.
• Passive cooling. The V100, P100 and A2 have no fans of their own: they rely on forced airflow from the server. In a home case you have to add cooling (a duct + a fan), otherwise the card will throttle. That is a job to be done, not "plug it in and it works".
• More difficult driver setup. This is data-centre hardware: configuring Tesla/Data Center cards is less typical than with GeForce and may require additional driver configuration; there is no classic "game ready". On Linux you can work comfortably, but the P100 and V100 need the R580 driver branch (the last that supports Pascal and Volta; 590 and newer drop them), so pin it instead of installing the newest driver; on Windows, where Teslas run in the compute-only TCC mode by default, it can take more of a fight.
• Power draw and noise. The V100/P100 draw ~250 W, plus the fan you have to add. The A2 is the exception on power (configurable 40–60 W), but it is passive too, so how quiet it is depends on the fan you give it.

And the most important sentence of this section, which marketing will never say: if you are just starting out, a consumer card is often simply more convenient. You put it in, plug in a monitor, install the normal driver and you are generating images within 15 minutes. With a Tesla you will spend an evening on cooling and configuration. For many people that evening is a bad deal, and it is better for them to know before the purchase, not after.

The hidden cost of an old Tesla: compatibility

The problem with the V100 and P100 is not only cooling or the lack of display output. It is also an older architecture from the software stack's point of view. The P100 is Pascal (compute capability 6.0) and the V100 is Volta (compute capability 7.0). In 2026, the latest AI toolchain is moving away from these architectures: CUDA 13 (August 2025) removed offline compilation for compute capability below 7.5, i.e. for Pascal and Volta among others. CUDA 12.x remains the last branch of NVIDIA's toolchain that can build for these architectures, but individual frameworks can restrict support even earlier: PyTorch removed Pascal support from CUDA 12.8/12.9 wheels in version 2.8 and Volta support in version 2.11. The last release with prebuilt wheels for these GPUs is 2.14 with CUDA 12.6. From 2.15 (October 2026), use a compatible older release, such as 2.14 with CUDA 12.6, or build PyTorch from source with a compatible CUDA 12.x toolkit, at least 12.6.

This does not mean the V100 has stopped working, but "no problem" is no longer guaranteed. llama.cpp and some tools built directly on CUDA can work very well; frameworks that depend on PyTorch require you to keep an eye on compatible versions of CUDA and the libraries. For the V100/P100, you now have to expect to use an older, increasingly legacy branch of the toolchain, sometimes pin a specific package version or build it yourself from source. The A2 (Ampere, compute capability 8.6) does not have this problem, and that is its real advantage over the older Teslas, alongside its power draw.

Hence the paradox: the V100 can be very fast in hardware, but it is less and less "modern" in software: its performance in our tests is excellent, while the availability of the latest ready-made library builds for Volta will become more and more limited over time.

This is an important part of the price of a used Tesla: you are buying not just a card, but an older software ecosystem. For someone who works on a stabilised stack, it does not matter. For someone who always wants the latest version of everything, it is real friction.

What you get with a consumer card, and where it loses

The RTX 3060 12GB has for years been the "people's" AI card: inexpensive, with display outputs, it runs on the normal driver and has very broad support in the popular local-AI ecosystem (Ollama, ComfyUI, Automatic1111 among others). The downside: only 12 GB. It is enough for SDXL; some 13B models in Q4 can fit, but with a longer context the memory headroom disappears quickly, and larger models exceed the available memory sooner than on 16 GB.

The RTX 4060 Ti 16GB gives the coveted 16 GB and low power draw (~165 W), with the full convenience of consumer hardware. The catch: a narrow memory bus (128-bit) limits bandwidth, so in bandwidth-sensitive tasks (some LLM inference) it may not shine as much as the "16 GB" figure would suggest. But as a whole package: monitor, driver, quiet, a manufacturer's warranty on a new unit: full convenience. Its successor, the RTX 5060 Ti 16GB, keeps the 128-bit bus but uses GDDR7 (448 GB/s), so it is the stronger new 16 GB option at this price; the V100's HBM2 still has twice its bandwidth.

Where does a consumer card lose to a Tesla? On the VRAM/FP16-to-price ratio in this budget and on a design intended for work in a server environment. A V100 16GB at around PLN 2,000 can give you HBM2 memory and Tensor Cores in a configuration that is hard to find among new 16 GB cards in a similar budget. If the card is going to work non-stop as the "engine" of an inference server, data-centre hardware is designed for that. A caveat: this is an argument about the class of design, not a guarantee: with a used unit what counts is its actual technical condition (its working history, temperatures, the condition of the HBM), which is why every card goes through a burn-in with us before sale.

Quick cheat sheet: need → card

Need	Best choice
Maximum measured AI performance in our tests	V100 16GB
Lowest power draw (24/7 server)	A2 16GB
FP64 / scientific computing (HPC)	V100 16GB (7 TFLOPS FP64); P100 16GB (4.7 TFLOPS) on the smallest budget
One card: monitor + AI	RTX 4060 Ti 16GB
Low card purchase cost	P100 16GB as a budget option (with Pascal limitations and additional cooling costs); for a regular PC, also compare current RTX 3060 12GB offers
First card for local AI	RTX 3060 / 4060 Ti

This is a qualitative recommendation; the final choice depends on the specific price, workload and software requirements.

Verdict: when a Tesla, when a consumer card

Choose a used data-centre card (V100 16GB / P100 16GB / A2 16GB) when:

• the card is going to sit in a server or PC running 24/7 as a dedicated AI engine,
• you have (or will build) cooling and a second card/iGPU for display,
• you care about maximum VRAM and FP16 for the budget, not convenience,
• you work on Linux and are not afraid of configuration.

Among these three: the V100 16GB is the strongest of them in our inference tests and the most sensible in hardware terms for experiments and for training models that fit in 16 GB of VRAM (though you have to reckon with the growing limitations of the modern CUDA/PyTorch stack for Volta); the P100 16GB when you need FP64/HPC on a tight budget (the V100 has higher FP64 performance but costs more); the A2 16GB when the priority is low power draw and a low profile in a small case with the required airflow through its passive heatsink.

Choose a consumer card (RTX 3060 12GB / 4060 Ti 16GB) when:

• it is your first time with local AI and you want "plug it in and it works",
• the same card has to drive a display (gaming, working on a monitor),
• you work on Windows and do not want to fight with drivers,
• you value quiet and simplicity over the last few gigabytes of VRAM.

If you are weighing up specific LLMs and their memory requirements, I have covered that separately in how much VRAM for Llama 7B/13B/70B and in the best GPU for LLM inference. And if you are considering used data-centre hardware at all, read the checklist for buying a used card first: it will save you from a few typical blunders.

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An honest disclaimer: yes, we sell used data-centre cards, so I have an interest in this. I have nevertheless tried to show the drawbacks as clearly as the advantages, because a return from a dissatisfied customer costs more than an honest recommendation. This is used hardware: every card goes through a burn-in test under full load with us before it reaches you; ask for the test report of the specific unit. Not sure which option fits your case? write to us: we will ask about your use case and tell you honestly whether it is worth taking a Tesla at all, or whether you are better off staying with a consumer card.

Sources

• NVIDIA Tesla V100: technical data (HBM2 VRAM, TDP, Tensor Cores): https://images.nvidia.com/content/technologies/volta/pdf/tesla-volta-v100-datasheet-letter-fnl-web.pdf
• NVIDIA Tesla P100: technical data (HBM2 VRAM, FP16 2:1, FP64, TDP): https://images.nvidia.com/content/tesla/pdf/nvidia-tesla-p100-PCIe-datasheet.pdf
• NVIDIA A2: technical data (16 GB GDDR6, ~60 W, low-profile): https://www.nvidia.com/content/dam/en-zz/solutions/data-center/a2/pdf/a2-datasheet.pdf
• TechPowerUp: GeForce RTX 4060 Ti 16 GB (128-bit bus, ~165 W power draw): https://www.techpowerup.com/review/nvidia-geforce-rtx-4060-ti-16-gb/
• TechPowerUp: GeForce RTX 3060 12 GB (12 GB GDDR6, 192-bit, ~170 W): https://www.techpowerup.com/gpu-specs/geforce-rtx-3060-12-gb.c3682
• NVIDIA CUDA Toolkit 13.0: Release Notes (removal of offline compilation for compute capability < 7.5: Maxwell/Pascal/Volta): https://docs.nvidia.com/cuda/archive/13.0.1/cuda-toolkit-release-notes/index.html