RTX 5070 Ti vs RTX 5090 for Local LLMs: 16GB vs 32GB

Key Takeaways

• The RTX 5090 carries 32 GB of GDDR7 on a 512-bit bus; the RTX 5070 Ti carries 16 GB on a 256-bit bus (TechPowerUp, TechPowerUp).
• A 32B dense model at Q4_K_M is roughly 19–20 GB of weights before any KV cache — it does not fit 16 GB, and that single fact drives the whole decision.
• The memory bandwidth gap is about 2x (1,792 GB/s vs 896 GB/s per TechPowerUp), which sets the first-order ceiling on generation speed for any model that fits both cards.
• At 128K context, a 32B-class model's KV cache alone can exceed 30 GB in fp16 — long context breaks a 16 GB card long before the weights do.
• The RTX 5090's 575 W TGP is nearly double the RTX 5070 Ti's 300 W (NVIDIA), which changes your PSU, your case airflow, and your 24/7 electricity bill.
• If your daily driver is an 8B or 14B model, a $300-class RTX 3060 12GB already holds it in VRAM and neither Blackwell card buys you anything you will notice.

Step 0: is your bottleneck VRAM or tokens per second?

Before you price either card, run the diagnostic on your current setup. It takes five minutes and it decides the entire purchase.

Load the model you actually use most. Watch VRAM utilization while it generates. Three outcomes:

The model is fully resident and you are happy with the speed. You have no hardware problem. Buying a Blackwell card will make a fast thing faster and change nothing about what you can do. Spend the money on more system RAM or a bigger model-library drive instead.

The model is fully resident and the speed annoys you. This is the only case where the throughput comparison between these two cards is the relevant one, and it is rarer than people think. Generation on a resident model is bandwidth-bound, so the roughly 2x bandwidth difference between the two cards is roughly what you will feel.

Your back-end is offloading layers to system RAM, truncating your context, or you have quietly dropped to a lower quantization to make something fit. This is the VRAM ceiling, and it is the case that justifies the RTX 5090. Everything below is written for you.

When this framing is right: single-user, interactive, one model at a time — which describes almost all local inference. When it isn't: if you are serving concurrent requests with a batching back-end like vLLM, throughput scales with compute in a way it does not for single-stream chat, and the calculus shifts toward the bigger die independent of the memory ceiling.

What do you actually gain from 32 GB over 16 GB?

The spec delta, drawn from NVIDIA's product pages and TechPowerUp's database:

Specifications per NVIDIA, TechPowerUp — RTX 5090, TechPowerUp — RTX 5070 Ti and TechPowerUp — RTX 3060 12 GB.

Two things stand out. First, memory capacity and memory bandwidth both scale 2x, because they come from the same source: the 512-bit bus is twice as wide as the 256-bit one. You cannot buy one without the other, which is why "just get more VRAM" is not a cheap option on consumer silicon. Second, the price scales 2.7x against a 2x capacity gain, so the RTX 5090 is worse per gigabyte. That is fine if the extra gigabytes unlock a model class you cannot otherwise run, and terrible if they do not.

Note that MSRP and street price have been badly decoupled for the 32 GB card. As of September 2026, retail listings for RTX 5090 boards routinely sit well above the $1,999 figure NVIDIA published, while RTX 5070 Ti boards are much closer to their $749 reference. Check the live price before you use any of the per-dollar math below — it gets worse for the 5090 the further street price drifts from MSRP.

Which models fit on 16 GB, and which need 32 GB?

This is the table the whole article exists for. Sizes are for a 27–32B parameter dense model — the class that sits exactly on the boundary between the two cards. Weight sizes follow the standard GGUF quantization tiers documented in the llama.cpp repository; add KV cache on top of every row.

Read the middle two columns as the answer. The RTX 5070 Ti's 16 GB tops out at a 14B model at q6_K, or a 20–24B model at q4_K_M with a modest context window. The RTX 5090's 32 GB is the first consumer card that holds a 32B-class model at q4_K_M with room left for a real context window, and it also comfortably hosts the sparse 20–35B-total mixture-of-experts releases at a higher quantization than a 16 GB card can manage.

If your shortlist is 8B and 14B models, the 16 GB card is over-provisioned already and the 32 GB card is a waste. If your shortlist has a 30B-class model on it, the 16 GB card is not a smaller version of the right answer — it is the wrong answer.

How much does generation throughput actually differ, and what happens to prefill?

Two separate phases, two separate bottlenecks, and conflating them is why published tok/s numbers vary so wildly between reviewers.

Generation (decode) emits one token at a time. For each token the back-end reads the entire set of active weights out of VRAM. That makes it almost purely memory-bandwidth-bound, and it gives you a clean first-order ceiling: tokens per second cannot exceed memory bandwidth divided by the resident model size. Using the bandwidth figures from TechPowerUp above, a 12 GB resident model has a theoretical ceiling of roughly 149 tok/s on the RTX 5090's 1,792 GB/s and roughly 75 tok/s on the RTX 5070 Ti's 896 GB/s. Real back-ends land meaningfully below both — the arithmetic sets an upper bound, not an expectation — but the ratio between the two cards holds, and it is about 2x.

Prefill (prompt processing) ingests your whole prompt in parallel. It is compute-bound rather than bandwidth-bound, so it scales with the tensor-core throughput of the die rather than the bus width. The RTX 5090's GB202 die carries far more of that silicon than the RTX 5070 Ti's GB203 (TechPowerUp), which is why the gap between the two cards is widest on the phase that most single-token benchmarks never measure.

The practical consequence: if you paste a 20,000-token document and ask a question, the delay before the first token appears is dominated by prefill, and that is where you feel the RTX 5090. If you chat in short turns, prefill is negligible and you are living in the 2x bandwidth ratio. Community measurements published in the r/LocalLLaMA hardware threads consistently show this split, with long-context summarization workloads separating the two cards far more than conversational ones.

What does context length do to the VRAM budget?

Weights are the fixed cost. The KV cache is the variable one, and it is the budget nobody plans for.

The arithmetic is public and follows directly from the transformer architecture the back-ends implement (llama.cpp): bytes per token equals 2 (one key, one value) x layers x KV heads x head dimension x bytes per element. For a typical 32B-class model with 64 layers, 8 grouped-query KV heads and a head dimension of 128, at fp16 that is 2 x 64 x 8 x 128 x 2 = 262,144 bytes, or 256 KB per token.

Two escape hatches exist and both cards benefit. Quantizing the KV cache to 8-bit halves those numbers at a small quality cost, and most back-ends expose it as a flag. Models with fewer KV heads or a compressed attention scheme cut it further. But the shape of the table does not change: long context is the second budget, it grows linearly with prompt length, and on a 16 GB card it is what breaks first.

Does the RTX 3060 12GB still make sense at a third of the price?

For a large fraction of readers, yes — and this is the section the GPU vendors would rather you skipped.

The ZOTAC Gaming GeForce RTX 3060 Twin Edge 12GB and the MSI GeForce RTX 3060 Ventus 2X 12G both carry 12 GB of GDDR6 on a 192-bit bus with 360 GB/s of bandwidth and a 170 W TGP (TechPowerUp). That 12 GB comfortably holds an 8B model at q6_K with a large context window, or a 13–14B model at q4_K_M with a tighter one. For the enormous number of people whose local LLM use is coding assistance, summarization and chat against models in that size range, the model is fully resident, the card is not the bottleneck, and both Blackwell cards are solving a problem they do not have.

Where the 12 GB tier stops: 20B-plus dense models, long context on anything above 8B, and image generation at high resolutions. The two cards differ mainly in cooler design and clearance rather than silicon — the two are set side by side in the ZOTAC Twin Edge vs MSI Ventus piece, and the full model-fit map lives in the RTX 3060 12GB local LLM guide.

The honest framing: the RTX 3060 12GB is the cheapest way to find out what you actually need. Run it for two months, note every time you hit the ceiling, and buy the Blackwell card that clears the specific ceiling you hit — if you hit one.

What CPU, RAM and PSU does each card need?

CPU. For a fully resident model the CPU barely matters; it feeds the GPU and gets out of the way. It matters enormously the moment you offload layers, because offloaded layers execute on CPU cores against system memory bandwidth. The AMD Ryzen 7 5800X is the sensible host for offload-heavy work — eight cores and sixteen threads on a 105 W TDP (AMD) is comfortably past the point where dual-channel DDR4 saturates, which is the actual ceiling. If the box is a 24/7 always-on inference host and never offloads, the AMD Ryzen 5 5600G at 65 W does the same job for less idle power. The offload case is set out in the CPU-offload host comparison.

System RAM. 32 GB is the floor for a 16 GB card, 64 GB for a 32 GB card. The rule of thumb is at least twice your VRAM, because loading a model streams the whole file through page cache before it lands in VRAM, and because any offload path needs somewhere to put the layers.

PSU. This is where the RTX 5090 costs you more than the sticker. At a 575 W TGP (NVIDIA), plan on a 1000 W-class ATX 3.1 unit with a native 12V-2x6 connector. Transient spikes on high-TGP cards can briefly exceed the rated draw by a wide margin, and first-generation ATX 3.0 units that already had trip complaints under 4090-class loads are not a safe base. The RTX 5070 Ti's 300 W is comfortable on a quality 750 W unit with no drama. Sustained inference also runs the card near its power limit for hours rather than minutes, so case airflow matters more than it does for gaming — see the 24/7 rig cooling comparison.

Where should the model library live?

Model weights are read once at load and then live in memory, so drive speed affects startup time and nothing else. That makes capacity the right thing to buy.

A multi-model library gets large fast: keeping three 30B-class models at two quantizations each is comfortably 100 GB. The Crucial BX500 1TB rates up to 540 MB/s sequential read and the Kingston A400 960GB up to 500 MB/s — both SATA-class, both cheap per gigabyte, and both entirely adequate here. At 500 MB/s, a 20 GB model file takes roughly 40 seconds on a cold load and effectively zero on a warm one once the OS page cache holds it.

An NVMe drive cuts that cold load to a handful of seconds and changes nothing about tokens per second. If you switch models constantly, that is worth paying for; if you load one model in the morning and use it all day, it is not. The tradeoff is broken down in the NVMe vs SATA model-library comparison.

Perf-per-dollar and perf-per-watt: which card is cheaper per token?

Using MSRP and the bandwidth-derived generation ceiling from above, and holding the model constant at something that fits both cards:

The RTX 5070 Ti wins every per-dollar row and the RTX 5090 wins the raw-capability rows, which is the normal shape of a flagship comparison. Perf-per-watt is close to a wash — the RTX 5090 is slightly ahead on bandwidth per watt, because Blackwell's efficiency did not regress, it just scaled up. What that means for a 24/7 box is that the RTX 5090 is not inefficient, it is simply large: running it hard around the clock is several hundred watts of continuous draw, and at typical US residential rates that is a real annual number you should put in the spreadsheet before you buy.

None of these rows change the conclusion, because per-dollar efficiency is irrelevant when the cheaper option cannot run your workload at all. Perf-per-dollar decides between two cards that both work. Capacity decides whether you have two options.

Verdict matrix

Get the RTX 5070 Ti if… your models live in the 8–20B range, you chat in short turns rather than pasting long documents, you want a card that also games at 1440p and 4K without a 1000 W supply, or you are building your first dedicated inference box and are not yet sure what you will run. 16 GB and 896 GB/s is a lot of card for $749.

Get the RTX 5090 if… you have already hit the 16 GB ceiling on a model you actually want, you run 27–32B class models at q4_K_M or better, you routinely work at 32K-plus context, or your workload is long-document prefill where the compute gap is widest. Budget the PSU and the airflow as part of the purchase, not as an afterthought.

Stay on an RTX 3060 12GB if… everything you run today is fully resident in 12 GB and you have never seen your back-end offload a layer. You do not have a hardware problem, and the budget local-LLM build is still the best dollars-per-usable-token in the catalog.

Buy two smaller cards instead if… you are running a batching back-end like vLLM with real concurrency, where tensor parallelism across two cards uses the aggregate memory well. For single-user llama.cpp layer splitting, do not — the inter-card transfer on every token gives back much of what the extra VRAM bought, and one contiguous 32 GB pool is both simpler and usually faster. If you do go multi-GPU on a budget, the dual RTX 3060 24GB build is the cheapest path to 24 GB.

The recommended pick

For the default reader — one person, one model at a time, a mix of coding and chat, wanting the machine to still be adequate in three years — the RTX 5070 Ti is the pick. 16 GB at 896 GB/s runs everything in the 8–20B band fully resident with room for a working context window, it does it on a 300 W budget that any decent 750 W supply already handles, and it leaves $1,250 in your pocket that buys system RAM, storage, and most of a second machine.

The counter-case is specific and worth stating plainly: if there is a particular 30B-class model you have already tried to run and could not, the RTX 5070 Ti will not run it either, and no amount of per-dollar reasoning changes that. Buying the cheaper card twice is the most expensive outcome available. If you know your ceiling, buy past it once.

Bottom line

This is not a performance comparison dressed up as a value comparison — it is a capacity decision with a performance gap attached. The RTX 5090's 32 GB is the first consumer memory pool that holds a 32B-class model at a good quantization with a real context window, and that is worth $1,999 to the people who need it and nothing at all to everyone else. Diagnose your ceiling first, on hardware you already own. Then buy the card that clears it, and not the one above it.

Related guides

• RTX 3060 12GB Local LLM Guide: Which Models Actually Fit
• Best Budget GPU for Local LLMs in 2026
• Best Parts for a Dual RTX 3060 24GB Local-LLM Build
• Ollama vs vLLM vs llama.cpp on a 12GB GPU
• RTX 3060 benchmark data · RTX 5090 benchmark data · RTX 5070 Ti benchmark data

Citations and sources

• NVIDIA — GeForce RTX 5090 (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 5090 specifications (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 5070 Ti specifications (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 3060 12 GB specifications (accessed 2026-09-09)
• AMD — Ryzen 7 5800X product page (accessed 2026-09-09)
• llama.cpp — GGUF quantization tiers and KV cache implementation (accessed 2026-09-09)
• r/LocalLLaMA — community hardware measurements (accessed 2026-09-09)

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported. Prices change frequently — check the current listing before buying.