ResearchAudio

Buyer comparison / 12 GB vs 16 GB

12GB vs 16GB VRAM for local LLMs: what four extra gigabytes change.

NVIDIA specifies 12GB of GDDR6X for the RTX 4070 Super and 16GB for the RTX 4070 Ti Super. That difference does not unlock every larger model: it moves a few exact 14B and 20B plans from a miss to a razor-thin paper fit.

Test your exact checkpoint →

Fast verdict

Buy 16GB for the model boundary, not for a vague “AI-ready” label.

RTX 4070 Super
12GB · 10.8 GiB planned

RTX 4070 Ti Super
16GB · 14.4 GiB planned

Same architecture
Ada Lovelace · GDDR6X

Capacity gained
3.6 GiB after reserve

Exact boundary checks

The 16GB card opens a narrow middle lane—not the 32B tier.

These plans include weights, an architecture-specific KV cache, and stated runtime headroom. Open any card to edit the checkpoint, context, concurrency, cache precision, reserve, or GPU count.

Qwen3 8B · INT4 · 32K · 12GB9.98 GiBFits with 0.82 GiB planned margin → Qwen3 14B · INT4 · 32K · 12GB14.27 GiBMisses by 3.47 GiB → Qwen3 14B · INT4 · 32K · 16GB14.27 GiBPaper fit with 0.13 GiB margin → gpt-oss-20b · MXFP4 · 4K · 16GB14.29 GiBPaper fit with 0.11 GiB margin → Qwen3 8B · INT8 · 32K · 16GB14.56 GiBMisses by 0.16 GiB → Qwen3 32B · INT4 · 32K · 16GB27.93 GiBNeeds a different memory path →

12GB vs 16GB decision

Four extra advertised gigabytes create 3.6 GiB of planned room.

The Evidence Lab reserves ten percent for the display stack, allocator, and runtime variability. Change that reserve when you have measurements from the exact machine.

ProfileRTX 4070 Super 12GBRTX 4070 Ti Super 16GB
Usable planning budget10.8 GiB14.4 GiB
Qwen3 8B INT4 · 32K9.98 GiB · fit9.98 GiB · more margin
Qwen3 14B INT4 · 32K14.27 GiB · miss14.27 GiB · paper fit
gpt-oss-20b MXFP4 · 4K14.29 GiB · miss14.29 GiB · paper fit
Qwen3 32B INT4 · 32K27.93 GiB · miss27.93 GiB · miss

A paper fit is not a promise. Packaged checkpoint overhead, backend allocations, desktop use, parallel requests, and a different cache layout can consume the remaining margin.

Capacity before speed

Twelve handles the 8B lane.
A well-quantized 8B model can retain useful context without offload in the stated scenario.

Sixteen opens a middle lane.
Some 14B and 20B plans clear the arithmetic boundary with almost no spare room.

Neither is a 32B answer.
The 32B example needs offload, less context, a smaller artifact, or more memory.

Primary source and method

Verify the card, then verify the checkpoint.

NVIDIA's family specification lists the RTX 4070 Super with 12GB GDDR6X on a 192-bit interface and the RTX 4070 Ti Super with 16GB GDDR6X on a 256-bit interface. ResearchAudio uses those capacities, exact binary-GiB arithmetic, editable cache inputs, and an explicit reserve. It does not infer LLM speed from gaming figures.

Official NVIDIA RTX 4070 family specifications → Compare another 12GB planning boundary → Inspect the detailed 16GB boundary →

Which is better for local LLMs, RTX 4070 Super or RTX 4070 Ti Super?

The 16GB RTX 4070 Ti Super is the stronger capacity choice. It supplies 14.4 GiB under this page's reserve versus 10.8 GiB for the 12GB RTX 4070 Super. Benchmark the exact workload before assigning a speed value.

What LLM can an RTX 4070 Super 12GB run?

The Qwen3 8B INT4 32K plan reaches 9.98 GiB and fits. The shown 14B and gpt-oss-20b plans exceed the default 10.8 GiB budget.

Can an RTX 4070 Ti Super 16GB run Qwen3 14B?

The stated 32K INT4 plan reaches 14.27 GiB, leaving 0.13 GiB under the default ceiling. Treat that as a paper fit and test the real backend.

Is 16GB VRAM enough for a 32B local LLM?

Not for the fully resident Qwen3 32B INT4 32K plan shown here. It reaches 27.93 GiB and requires a different memory strategy.

Hardware claims, checked

Get the next local-AI deployment teardown.

ResearchAudio traces exact checkpoints, context, runtime constraints, and the gap between “fits” and “works.”