Best local LLMs for the RTX 4070 Ti (12 GB) — updated July 2026
12 GB is a genuinely useful middle ground: dense models up to ~14B run fully in VRAM at
Q4–Q5, an 8B runs near-lossless at Q8_0, and with
expert offload a 35B-class MoE like Qwen3.6 35B-A3B is realistic. What
12 GB honestly can't do is a usable dense 70B.
Numbers you can trust
TurboLLM measures real tokens/sec on your RTX 4070 Ti — from actual generation,
not a spec sheet — and shows a VRAM-fit verdict before you load. Every speed on this page
is labeled with where it came from; where we have no measurement, we say so instead of
inventing one.
How this shortlist was built — the 12 GB blend
Our models hub tiers its picks at 8 GB and 16 GB — there's no 12 GB
tier, so this page blends the two: the 8 GB picks promoted to higher quants
(Q6/Q8 now fit fully, verified against the actual GGUF file sizes on Hugging Face), plus the
16 GB picks a quant lower or with CPU offload. One caveat up front: if you
own the RTX 4070 Ti SUPER, that's a 16 GB card — skip this page and use the
16 GB tier on the models hub directly.
12 GBRTX 4070 Ti — between the hub's 8 GB and 16 GB tiers
Small dense models at near-lossless quants, one 35B-class MoE via expert offload, and one dense quality pick that trades speed for it. Sizes are the on-disk GGUF at that quant.
Qwen3.6 35B-A3B
by Alibaba · GGUF
MoE · ~3B active
toolsthinkingcode
The 16 GB tier's daily driver still makes sense on 12 GB: as an MoE with only ~3B params active per token, it tolerates expert offload well — slower than on a 16 GB card, but far from crippled.
12 GB:Q3_K_M (16.6 GB) with experts offloaded to system RAM — attention + KV stay on the GPU · wants ~24 GB+ system RAM · 256K context
The 8 GB tier's all-rounder, promoted: on 12 GB it runs at Q8_0 near-lossless instead of Q4, with a toggleable thinking mode and headroom left for a long context.
12 GB:Q8_0 (8.7 GB) fully in VRAM · or Q6_K (6.7 GB) for maximum context headroom · 128K context
The quality pick — with an honest caveat. Dense models pay a real speed price for CPU offload, and Q3_K_M doesn't quite fit 12 GB, so this is the slowest card here. Pick it when answer quality matters more than tokens/sec; the MoE above is the better daily driver.
12 GB:Q3_K_M (13.6 GB) with a few layers on CPU — costs speed · fits fully only at a Q2-class quant, which we don't recommend · 256K context
The best dense coder that fits fully — and on 12 GB it fits at Q8_0, so you get its full quality. The newest coders are big MoE models that want 16 GB+.
12 GB:Q8_0 (8.1 GB) fully in VRAM · or Q6_K (6.3 GB) · 32K context
We haven't benchmarked an RTX 4070 Ti ourselves, so the table below is what other people's cards report — useful as a ballpark, not a promise:
Model · quant
Generation
Prompt processing
Llama 3.2 1B · Q4_K_M
~165 t/s
~12,800 t/s
Llama 3.1 8B · Q4_K_M
~60 t/s
~3,700 t/s
Qwen2.5 14B · Q4_K_M
~40 t/s
~2,200 t/s
community-reported, localscore.ai, 2025–2026 — averages across user submissions on RTX 4070 Ti cards.
Two honest footnotes. First, those rows are Q4 quants — the Q8_0 picks above read
twice the bytes per token, so expect them somewhat slower than the Q4 figures. Second, the only
numbers we've measured ourselves come from a different card: on an RTX 5070 Ti 16 GB,
Qwen3.6-35B-A3B at a 200K context ran at 74.7 t/s (q4_0 KV) in TurboLLM. That card
has more VRAM and offloads fewer experts, so don't map its number onto the 4070 Ti. When you load
any of these models, TurboLLM measures the real tokens/sec on your card and shows it in the model
list — that figure beats anything on this page.
How TurboLLM auto-fits the RTX 4070 Ti
Before you load a model, TurboLLM checks it against your card's real free VRAM and shows a
fit verdict — fits fully, fits with offload, or won't fit. On load,
auto-tune benchmarks generation on your GPU and derives fast
defaults: it sets the GPU-layer count for dense models and the expert-offload split for MoE
models automatically, so the Q3 35B-A3B above lands correctly on the first try instead of
after an hour of flag guessing.
FAQ
Can the RTX 4070 Ti run a 70B model?
Not usably. A 70B GGUF at Q4_K_M is about 43 GB, so roughly three quarters of the
model would sit in system RAM — and a dense model offloaded that heavily drops to a few tokens
per second. The smarter big-model path on 12 GB is a Mixture-of-Experts like
Qwen3.6 35B-A3B: only ~3B parameters are active per token, so it stays quick
with its experts in system RAM.
What quant should I use on 12 GB of VRAM?
For 7–8B models, take Q6_K or Q8_0 — they fit fully in 12 GB with room
for context. For 13–14B dense models, Q4_K_M or Q5_K_M. Anything bigger
needs a lower quant plus CPU offload, which MoE models tolerate far better than dense ones. The
naming is decoded in Quantization explained.
How fast will Qwen3.6 35B-A3B run on the RTX 4070 Ti?
We haven't measured this card, so we won't print a number. On an RTX 5070 Ti 16 GB we measured
74.7 tokens/sec at a 200K context; the 4070 Ti has less VRAM and offloads more experts to system
RAM, so expect noticeably less. TurboLLM benchmarks the model on your own card when you load it
and shows the real figure.
Do I need to compile anything to run local LLMs on the RTX 4070 Ti?
No. TurboLLM detects the RTX 4070 Ti, downloads a matching prebuilt CUDA
llama-server, and runs it — no CUDA toolkit, no Python environment, no compiler.
If you later want a community fork instead, the engines catalog
handles that too.
Is the RTX 4070 Ti SUPER covered by this page?
No — the 4070 Ti SUPER has 16 GB of VRAM, not 12 GB. It maps to the 16 GB tier on our
models hub: the same models at a higher quant, with the 27B dense fully
resident in VRAM.