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Buy for VRAM first, then compare speed and measured energy use. For most local-AI builders, the practical 16GB shortlist is the GeForce RTX 5060 Ti 16GB, GeForce RTX 5070 Ti 16GB, and Radeon RX 9070 XT. Choose the 5060 Ti when its current price is the lowest way to obtain 16GB; consider the 5070 Ti when a workload justifies a higher-performance tier; and consider the RX 9070 XT when its price and ROCm software support fit your system. If your model needs more than 16GB, move to a Radeon RX 7900 XTX (24GB) or GeForce RTX 5090 (32GB), accepting higher purchase and power costs. No verified evidence establishes one universal energy-efficiency winner, so compare tokens per joule on the exact model, quantization, context, runtime and power measurement you will use.
What should you prioritize when buying a GPU for local AI?
Start with the model and operating context, not a graphics-card ranking. A local language model uses memory for weights, the context or KV cache, runtime allocations and other GPU activity. A card that technically holds the weight file can still run out of VRAM when you increase context, use a larger batch or load another model.
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Use a capacity-first decision
- Define the workload. Write down the model family, parameter size, quantization, maximum context, batch size and inference engine. A single-user chat session has different requirements from parallel serving.
- Reserve headroom. Treat the model’s nominal file size as a starting point, not the entire VRAM budget. Lower-bit weights reduce storage, but cache and runtime overhead still grow with context and concurrency.
- Check the exact board. Confirm VRAM, cooler dimensions, power connectors, board power, manufacturer PSU guidance, return terms and current local price for the specific partner card.
- Compare matched measurements. Bandwidth and advertised AI figures are useful clues, but tokens per second and tokens per joule depend on the complete software and model setup.
How much VRAM do you need for local models?
VRAM sets the practical ceiling for a fully or mostly GPU-resident model. Quantization changes the weight footprint: in principle, a parameter count multiplied by bits per weight and divided by eight gives the raw weight storage, while metadata, kernels, context cache and other allocations require additional room. Therefore, “an 8B model fits in 8GB” is not a safe buying rule without stating precision, context and runtime.
When 16GB is the sensible target
Sixteen gigabytes is the key comparison tier in this market. It gives a useful margin over entry cards for quantized small and mid-sized models, but it does not guarantee that every larger model or long-context workload will remain entirely on the GPU. If your planned model leaves only a small amount of free memory after loading, expect compromises such as shorter context, smaller batches or CPU offload.
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When more than 16GB changes the purchase
Move to 24GB or 32GB when avoiding offload, supporting longer context, running larger quantized models or serving multiple requests matters more than minimizing the initial bill. Extra capacity can also delay another upgrade, but it does not automatically make a card efficient: you still need a workload-specific power measurement.
Which 16GB GPU is the best budget fit?
The three current 16GB candidates occupy different performance and price positions. Their official memory specifications are comparable; their real inference speed must be measured with the same model and settings.
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| GPU | VRAM | Listed memory bandwidth | How to interpret it |
|---|---|---|---|
| GeForce RTX 5060 Ti 16GB | 16GB | 448 GB/s | Value-oriented 16GB starting point; buy when its live board price is attractive for your workload. |
| GeForce RTX 5070 Ti 16GB | 16GB | 896 GB/s | Higher-bandwidth 16GB tier; justify the price premium with measured workload speed, not bandwidth alone. |
| Radeon RX 9070 XT | 16GB | Up to 640 GB/s | AMD alternative with 304W typical board power and an official ROCm listing; verify your exact software stack. |
The figures in this table are manufacturer specifications, not common-platform local-inference benchmarks. NVIDIA lists the RTX 5060 Ti at 448 GB/s and the RTX 5070 Ti at 896 GB/s. AMD lists the RX 9070 XT at up to 640 GB/s and 304W typical board power. A wider bus or higher bandwidth can help generation, particularly when a workload is memory-bound, but it cannot predict speed across different architectures and software.
Choose the RTX 5060 Ti 16GB when
- Your model fits comfortably in 16GB with the context you actually need.
- The current price difference to the RTX 5070 Ti is larger than the value of the extra throughput you can verify.
- Your preferred inference tools have a mature NVIDIA path and you value fewer compatibility variables.
Choose the RTX 5070 Ti 16GB when
- You are staying within a 16GB limit but want a higher-performance tier.
- Matched measurements show that its speed increase reduces waiting time enough to justify the price uplift.
- Your case, connectors and PSU can accommodate the exact board you plan to buy.
Consider the RX 9070 XT when
- Its live price is competitive with the 16GB NVIDIA options.
- You are prepared to validate the current ROCm version, operating system, framework and inference application.
- The card’s 304W typical board-power rating and the chosen board’s cooling and PSU requirements fit your system.
Which GPUs make sense when 16GB is not enough?
Higher-capacity cards solve a different problem: keeping a larger model, longer context or more concurrent work in memory. They are capacity options rather than automatic low-cost or low-power recommendations.
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| GPU | VRAM | Listed memory bandwidth | Rated power information | Best use of the extra capacity |
|---|---|---|---|---|
| Radeon RX 7900 XTX | 24GB | Up to 960 GB/s | 355W typical board power | More room for larger quantized models and context when ROCm compatibility is confirmed. |
| GeForce RTX 5090 | 32GB | 1,792 GB/s | Manufacturer board and PSU details vary by exact card | Workloads that genuinely require 32GB; not a default budget or low-power choice. |
AMD’s specifications give the RX 7900 XTX 24GB, up to 960 GB/s and 355W typical board power. NVIDIA lists the RTX 5090 with 32GB and 1,792 GB/s. Those numbers describe hardware capability, not energy consumed per generated token. If your model fits in 16GB, buying a 24GB or 32GB card solely for a larger bandwidth number may waste money and electricity.
Does memory bandwidth tell you which GPU is most efficient?
Bandwidth helps explain why one card may generate tokens faster, but efficiency is a ratio: useful output divided by energy used. A high-bandwidth card can finish a request sooner yet draw more power while doing so; a lower-power card can be slower but use less total energy, depending on utilization and software.
Separate the measurements
- VRAM capacity determines whether the workload can run as configured.
- Memory bandwidth is a hardware limit that can matter for memory-bound generation.
- Board power is a rated electrical design value, not the energy required for a task.
- Tokens per second is a performance result tied to model, quantization, context, runtime, batch and settings.
- Tokens per joule combines output and energy and is the most relevant efficiency measure for a fixed job.
Do not substitute gaming power charts, TDP or TBP, AI TOPS or raw bandwidth for a local-inference energy result. AMD’s 304W and 355W figures are useful for sizing and heat planning, but they do not state how many watt-hours either card needs to produce a given answer.
How can you measure tokens per joule at home?
A simple, repeatable test is more useful than a generic ranking. Keep every variable fixed except the GPU.
- Choose one model and quantization. Record the exact file or build, including quantization format.
- Fix context and prompt. Use the same prompt set, maximum context and generation length for every card.
- Use one runtime version and configuration. Record the inference engine, drivers, framework, batch size, GPU layers and any CPU offload.
- Measure output. Capture generated tokens and elapsed time after warm-up, then calculate tokens per second.
- Measure power consistently. A wall meter captures the whole system; GPU telemetry captures the card. Do not mix methods between tests.
- Calculate energy efficiency. Convert measured power and elapsed time into watt-hours, then divide generated tokens by watt-hours. Repeat enough runs to smooth startup and background variation.
- Report the conditions. Include operating system, driver, room or fan settings if relevant, model, context, runtime and measurement point.
A 2026 preliminary paper tested 18 open-source models from 0.5B to 7B parameters on one RTX 4060 Ti 16GB. That establishes a task-focused power study, but the available abstract does not provide enough protocol or detailed figures to rank the cards in this guide. A separate comparison database reports Llama 3.1 8B Q4_K_M throughput for some GPUs and marks others without a recorded result; those figures are publisher-specific throughput records, not energy benchmarks.
How does software support affect the AMD-versus-NVIDIA choice?
Software can determine whether the hardware is usable at all. AMD’s ROCm documentation lists the RX 9070 XT and RX 7900 XTX, but support depends on the exact operating system, current ROCm release, framework and inference tool. Confirm that chain before buying, then test installation with the model and backend you intend to run.
NVIDIA cards may be the lower-friction option for a workflow whose documentation, prebuilt packages or community troubleshooting assumes NVIDIA drivers. That is a workflow consideration, not proof that every NVIDIA card is faster or more efficient. Check driver compatibility and the runtime’s supported compute backend for the specific model.
How should you treat prices in a 2026 budget?
Use the price you can actually pay today, not a launch MSRP. A 2026 market report describes street-price movement away from launch pricing, so the ranking can change by country, retailer, board partner and week. Compare complete transaction cost: card price, PSU or cooling upgrades, electricity, warranty and the cost of buying more VRAM later.
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Set a maximum budget for the card, then set a separate allowance for system changes. A cheaper card that requires a new PSU or cannot run your chosen backend may cost more in practice. Conversely, paying for a 32GB flagship is difficult to justify if your workload remains comfortably inside 16GB.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is a used RTX 3090 worth investigating?
A used RTX 3090 is commonly discussed as a 24GB local-AI option. It can be relevant when memory capacity is the priority, but current used prices, seller condition and warranty were not verified here. Treat it as a lead to investigate rather than a proven best-value recommendation.
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What should you check before placing the order?
- VRAM capacity against the model, quantization, context and concurrency you will run.
- Current price for the exact board SKU in your region.
- Memory bandwidth as a comparison clue, not a promised tokens-per-second result.
- Rated board power, connector type, manufacturer PSU recommendation and cooling clearance.
- Operating-system, driver, framework and inference-runtime support.
- Measured or reproducible performance under matched conditions.
- Warranty, return policy and, for used cards, seller condition and proof of stability.
- Expected electricity and heat for the hours you plan to run local inference.
Frequently Asked Questions
Can a 16GB GPU run a 70B language model locally?
It depends on the quantization, context and whether you accept CPU or multi-GPU offload. A 16GB card generally cannot be assumed to hold a 70B model entirely in VRAM; calculate the weight footprint and leave room for cache and runtime allocations before buying.
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Is a lower-wattage GPU always cheaper to operate?
No. Electricity cost depends on energy consumed over the whole job, not just the card’s rated board power. A faster card may finish sooner, while a slower card may draw less; measure watt-hours for the same output to know which costs less.
Do I need Linux to use ROCm with a Radeon card?
Not necessarily, but support is version- and tool-dependent. Check the current ROCm documentation for your operating system, framework and inference application rather than assuming that a listed GPU works identically on every platform.
What is the safest way to buy a used GPU for local AI?
Prefer a seller with a meaningful return period and verify the exact board, warranty status and operating history. Run a sustained inference or stress test immediately after purchase and keep records of temperatures, crashes and fan behavior while the return option remains open.
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