RTX 5070 for Local AI: What 12GB Runs, and When to Pay for 16GB

Short answer: the RTX 5070 is the fastest 12GB card you can buy new for local AI — 672 GB/s of GDDR7 pushes an 8B model to ~59 tok/s even at a 16K context (per Hardware Corner's llama-bench runs) — but it is a 14B-ceiling card, and as of early August 2026 it costs more on the street ($699.99 lowest at Newegg) than the 16GB RTX 5060 Ti ($599.99). If local LLMs are the main reason you're shopping, buy the 16GB card. Buy the 5070 if you split the card with serious gaming or live entirely in the fast 7B-9B class.

That price inversion is the story of this page. On MSRP paper ($549 vs $429) the 5070 reads like the sensible mainstream step-up. In the actual memory-shortage market, the 12GB card costs $100 more than the 16GB card, which turns the classic "speed vs capacity" dilemma into something much easier to answer. Below: verified specs, what actually fits in 12GB, published benchmarks (no invented numbers), and exactly where the 5070 still wins.

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Verified Specs

The one number that matters most for LLMs is 672 GB/s of memory bandwidth — the most on any current 12GB card. (The discontinued RTX 3080 Ti 12GB shipped 912 GB/s of GDDR6X back in 2021 — the used-market caveat we come back to below.) Token generation streams the whole model out of VRAM for every token, so bandwidth, not core count, sets your tok/s.

Specs below are from NVIDIA's official product page and the card's launch coverage:

Context inside its own family: the 5070 has 33% more CUDA cores and 50% more bandwidth than the RTX 5060 Ti 16GB, but 4GB less VRAM. Against the last-gen card it replaced, the RTX 4070's 12GB of GDDR6X is the same capacity at meaningfully less bandwidth — which is why the 5070 wins every same-size speed test but runs the exact same model list. If you already own a 4070 or a 3060 12GB, nothing on this page unlocks new models for you; see what your 12GB card already runs before spending anything.

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What It Costs Right Now

As of early August 2026, the cheapest in-stock RTX 5070 at Newegg was $699.99 (Gigabyte and PNY models) — 27% over the $549 MSRP — with other listings running $759 to $1,158. Street prices move weekly; treat these as a snapshot, not a promise.

The memory shortage that pushed the RTX 5090 past $3,600 did not spare the mid-range. Here is the mainstream board as we found it (lowest in-stock Newegg listings, early August 2026):

Read that table twice, because it decides most purchases: the 16GB 5060 Ti is now $100 cheaper than the 12GB 5070. At MSRP the 5070's speed premium cost you $120 and you gave up 4GB. At street, the capacity card is the cheaper card. Inflation hit the 5060 Ti harder in percentage terms (16GB of GDDR7 is exactly what the shortage makes expensive), but the dollar gap still favors it.

One more data point for the "just stretch a bit further" crowd: the 5070 Ti at $989.99 gets you 16GB and 896 GB/s bandwidth — but that is a $290 jump and a different budget conversation, covered in our 5070 Ti vs 4090 comparison.

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What Fits in 12GB

12GB is the 14B sweet spot: ~9GB of Q4 weights plus 2-3GB of KV cache is exactly the budget. These fit figures come from our own VRAM calculator data and match what we publish across the 12GB tier:

Two practical rules from running this tier daily. Stay at Q4_K_M — a 14B at Q5 (~11GB) technically loads but leaves no room for context on 12GB. And keep num_ctx at 8K or below with a 14B resident; when a task genuinely needs a long context, drop to the 8B and enjoy the headroom.

The full ranked picks, with pull commands and per-model notes, live on our best Ollama models for 12GB VRAM page — and if you code, the 12GB coding-model picks are the same exercise for Qwen 2.5 Coder 14B and friends.

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How Fast Is It

Published numbers, attributed — none of these are ours. Hardware Corner's GPU ranking (llama-bench, CUDA 12.8, Q4_K_XL quant, 8B model, 16K-token context) puts the 5070 at the top of the current-generation 12GB class — with one used-market exception worth seeing in the same table:

Note that 16K-context figure is the pessimistic case — a long context drags generation down. At short contexts the same card flies: GPU Battle's July 2026 runs (llama.cpp, Q4_K_M, cloud-hosted cards) measured 119.9 tok/s on Llama 3.1 8B and 180.2 tok/s on Qwen3 4B. Both sources agree on the ordering; the absolute numbers differ because context length and quant differ. Real-world daily use lands between the two.

For the 14B class we won't quote a 5070 number nobody has published. Our own 12GB-tier testing puts the RTX 4070 and 3060 12GB at ~25-32 tok/s on Phi-4 14B at Q4, and Hardware Corner's runs show the 5070 about 14% ahead of the 4070 on the same workload — so low-to-mid 30s tok/s is the honest expectation, which reads faster than you can. For calibration: chat feels real-time from roughly 15-20 tok/s.

Where the 5070 is not special: anything that fits in 12GB also fits in a used RTX 3090's 24GB, and the 3090's 936 GB/s bandwidth beats the 5070 by ~48% in the same table above. If you can stomach the used market, the 3090 remains the value king — it runs 32B models the 5070 cannot load at all.

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Image Generation on 12GB

SDXL runs comfortably: GPU Battle measured 3.03 it/s on the 5070 — call it a 1024x1024 image every ~10 seconds at typical step counts. SD 1.5 is trivial. The 6,144 Blackwell cores matter more here than in LLM work, since diffusion is compute-bound rather than bandwidth-bound.

Flux is the honest asterisk at this tier. The full-precision Flux dev model wants more than 12GB, so on a 5070 you run the FP8 or GGUF-quantized variants — which have gotten genuinely good, and which we keep ranked with exact file recommendations on our best Flux model for 12GB VRAM page. If image generation is your primary workload rather than a side quest, that changes the GPU math in ways LLM benchmarks don't capture — 16GB buys quality headroom (higher-precision Flux, bigger batches, ControlNets resident alongside the base model) more than it buys speed.

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RTX 5070 vs RTX 5060 Ti 16GB: The Real Question

For LLM-first buyers in August 2026: the 5060 Ti 16GB, and it isn't close — it's $100 cheaper and runs a whole model tier the 5070 can't. Here's the full picture:

The way to think about it: the 5070's advantage is a ~15% speed bump on models both cards run. The 5060 Ti's advantage is an entire tier of models the 5070 will never load — OpenAI's gpt-oss:20b was sized for 16GB cards specifically, Devstral 24B fits for agentic coding, and the 14B class gets Q5 quant plus long context instead of a tight Q4 squeeze. Capacity is a hard wall; speed is a preference. When the capacity card is also the cheaper card, the argument mostly ends. Our full 5060 Ti 16GB review has the deeper dive, including why a used 3090 still beats both on raw value.

The 5070 still wins for two buyers. First: the gamer who runs local AI on the side — the 5070 is simply the better graphics card, and 12GB covers the 7B-14B assistant work most people actually do. Second: the speed-first user who lives in the 7B-9B class (fast drafting, RAG over modest contexts, coding autocomplete) and wants every token per second — at short contexts that 672 GB/s is the class of the field, per GPU Battle's 119.9 tok/s measurement. If that's you, nothing at this price generates faster.

Not sure which bucket you're in? Our which-GPU picker walks the same decision tree interactively.

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The 5070 Super Delay (Why 12GB Stays "Current")

The card that would make this whole dilemma obsolete — an RTX 5070 Super with a leaked 18GB — is delayed with no official date, most recently reported slipping to CES 2027.

What the supply-chain reporting says (none of this is an NVIDIA announcement): the 50-series Super refresh exists and reached board partners, but the launch is on hold because 3GB GDDR7 modules — the parts that make 18GB-on-192-bit possible — reportedly cost around three times as much as the 2GB modules, in the middle of the same memory shortage inflating every card above. Reported timing has slid from CES 2026 to Q3 2026 to, in the most recent coverage, CES 2027. Delayed, per a prominent leaker — not cancelled.

Two takeaways for a buyer holding $600-700 today. One: don't wait on a leak — a card with no date is not an option, and the shortage that delayed it shows no sign of resolving on your schedule. Two: this delay is exactly why the 12GB 5070 stays the current mainstream NVIDIA card deep into 2026 rather than being refreshed away — your purchase is not about to be obsoleted next quarter. (We'll update this section when the Super lineup actually ships.)

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Setup in 10 Minutes

Got the card? Here's the fastest path to a running local model — same commands we publish across the 12GB tier:

# Install Ollama (Linux/WSL2; macOS/Windows installers at ollama.com)
curl -fsSL https://ollama.com/install.sh | sh

# The 12GB all-rounder
ollama pull qwen3:14b
ollama run qwen3:14b

# The reasoning standout and the speed lane
ollama pull phi4
ollama pull llama3.1:8b

After the first response, run ollama ps and confirm it reports 100% GPU — if you see a CPU split, the model plus context outgrew VRAM and your tok/s will fall off a cliff. Drop the context size or the quant. Full pick-by-task rankings for the card live on our 12GB Ollama models page.

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Honest Limitations

• The 14B ceiling is real and you will feel it. The most interesting open-weight releases of the past year cluster at 20B-32B (gpt-oss:20b, Devstral, the Qwen coder line). On a 5070 you read about them; on 16-24GB you run them.
• 12GB of GDDR7 at $700 is a shortage-market price. You're paying 27% over MSRP for a card whose MSRP already drew "should have been 16GB" reviews at launch. We flagged the same dynamic across the whole market in the memory-shortage buying map.
• Long contexts squeeze the KV cache hard. With a 14B resident there's ~3GB left; 8K context is the practical limit before offloading starts eating your speed.
• Our 14B speed figure is an extrapolation. Nobody we trust has published 5070 14B tok/s; we derived low-to-mid 30s from the 4070's measured ~25-32 plus Hardware Corner's measured 5070-vs-4070 gap. Treat it as an estimate until we can measure it ourselves.
• 250W TGP is fine but unremarkable. The 5060 Ti does its work at 180W — quieter, cooler, smaller PSU. For an always-on home AI box that runs while you sleep, that difference compounds.

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Verdict

The RTX 5070 is a good card wearing the wrong price tag for this specific job.

1. If local LLMs are the point: buy the RTX 5060 Ti 16GB instead. It's $100 cheaper on the street ($599.99 vs $699.99, early August 2026), runs everything the 5070 runs, and adds the 16GB tier — gpt-oss:20b, Devstral 24B, Q5 quants — that the 5070 is permanently locked out of.
2. If you game first and run AI second: the 5070 is the right card. It's the better GPU, and 12GB genuinely covers daily 7B-14B assistant work at the fastest speeds in its class.
3. If you want maximum capability per dollar: a used RTX 3090 still embarrasses both — 24GB, 936 GB/s, 32B-class models, typically under a grand used.
4. Don't wait for the 18GB 5070 Super. Reported delayed to CES 2027 over memory costs, with nothing official. A card with no date is not a plan.

The 5070's engineering is honestly impressive — the most bandwidth on any 12GB card you can buy new. But local AI in 2026 is a capacity game, and the market just made the capacity card cheaper.

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Sources

• NVIDIA RTX 5070 family page — official specs (cores, memory, TGP, AI TOPS)
• Hardware Corner GPU ranking for local LLMs — llama-bench token-generation measurements (CUDA 12.8, Q4_K_XL)
• GPU Battle RTX 5070 AI benchmarks — short-context llama.cpp and SDXL measurements (July 2026)
• Newegg in-stock listings, early August 2026 — street pricing snapshot
• Digital Foundry, NotebookCheck, Tech4Gamers, Guru3D — RTX 50 Super delay reporting (leak-grade, as labeled)
• llama.cpp — benchmark your own card with llama-bench for consistent quantization

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