Best local LLMs for the RTX 5070 Ti (16 GB) — updated July 2026
The RTX 5070 Ti's 16 GB of GDDR7 is the current sweet spot for local LLMs: a
27B dense model fits fully in VRAM at IQ4_XS, and a
35B-A3B MoE runs at Q3 with a light expert nudge to system RAM — or at Q4
with fuller expert offload, which also lets a huge agentic coder MoE run on this card. What it honestly won't do is a
dense 70B — that needs ~40 GB at a sensible quant. One more thing makes this page different:
this is the exact card TurboLLM is benchmarked on, so the speeds below are
measured, not guessed.
Numbers you can trust
Most GPU pages on the internet guess. This one doesn't have to: the RTX 5070 Ti is the card
TurboLLM's founder develops and benchmarks on, so every speed on this page was
measured on this exact GPU with TurboLLM's auto-tuner — labeled as such, head-to-head
against LM Studio on the same machine. And when you run TurboLLM yourself, it measures real
tokens/sec on your card and shows a VRAM-fit verdict before you
load — no guessed speeds in the app, ever.
The shortlist below is the models hub's 16 GB tier with the fit worked out for this card. Sizes are the on-disk GGUF at that quant.
Qwen3.6 35B-A3B
by Alibaba · GGUF
MoE · ~3B active
toolsthinkingcode
The daily driver — and the model behind this page's measured numbers: 74.7 t/s at a 200K context on this exact card. Only ~3B params active per token, so it stays quick even with experts spilled to CPU.
RTX 5070 Ti:Q3_K_M (16.6 GB) just spills past 16 GB — auto-fit nudges a few experts to system RAM — or Q4_K_M (22 GB) + expert offload · 256K context
The dense sibling — slower than the MoE but rock-solid quality, fully in VRAM at a mid quant. With the TurboQuant fork's turbo4 KV cache it held a 200K context on this card at a measured 24.6 t/s — 2.2× LM Studio on the same machine.
The newest agentic coding MoE. Its experts live in system RAM while attention and KV stay on the GPU, so it runs on this 16 GB card — TurboLLM's auto-fit sets the split.
RTX 5070 Ti:IQ2–IQ3 with heavy CPU-expert offload (needs system RAM) · even better on 24 GB+ · large context
The vision pick — current-gen vision-language model with strong OCR and spatial reasoning; a tiny footprint leaves most of the 16 GB free for images and context.
RTX 5070 Ti:Q4_K_M (5.0 GB) with plenty of headroom for images + context · 256K context
Every other GPU guide on this site quotes community reports, because we refuse to print a number
we didn't measure. This page is the exception: the RTX 5070 Ti 16 GB is the founder's own
benchmark card, so these are real generation numbers from this exact GPU,
run head-to-head against LM Studio on the same machine with the same model files, at a
full 200K-token context:
Founder-measured on an RTX 5070 Ti 16 GB, July 2026 — same machine, same GGUF files, 200K-token context in both tools. Full methodology on the TurboLLM vs LM Studio page.
Two stories in that table. The first two rows are official llama.cpp in both tools —
the gap comes from the build and the tuning: TurboLLM auto-provisioned a Blackwell-native
CUDA 13 engine and tuned the expert-offload split, while LM Studio ran its bundled runtime.
The 2.2× row is the fork story: TurboQuant's turbo4 KV format doesn't exist in
LM Studio's bundled runtime, so LM Studio ran the quality-matched option (q8_0 KV)
on the same dense 27B — being able to load the fork is the feature. If you're weighing
neighbors: the RTX 5080 runs the exact same 16 GB fits with more
bandwidth behind them, and the RTX 5060 Ti 16 GB runs them at a
budget card's pace.
Blackwell wants CUDA 13 — handled automatically
The 50-series is new enough that generic runtimes built for older CUDA can leave performance
on the table. TurboLLM detects the 5070 Ti and auto-provisions a CUDA 13,
Blackwell-native llama-server build — nothing to compile, no toolkit
install. That build gap is part of the 74.7-vs-61.0 result above.
How TurboLLM auto-fits the RTX 5070 Ti
Load any model and TurboLLM benchmarks it on your 5070 Ti, storing the measured
tokens/sec in the model list — live numbers from actual generation, exactly like the
table above. Before you load, every quant gets a VRAM-fit verdict against your
real 16 GB, including KV-cache growth at your chosen context. Flip the auto-fit
toggle and it picks the GPU-layer count and the MoE expert-offload split for you —
that's what put the 35B-A3B's idle experts in system RAM for the 74.7 t/s run. Details in
the auto-tune docs.
FAQ
How fast will Qwen3.6 35B-A3B run on the RTX 5070 Ti?
This is the one card we can answer with a measurement instead of a guess: on this exact GPU,
Qwen3.6 35B-A3B ran at 74.7 tokens/sec with a q4_0 KV cache and
72.3 tokens/sec with a q8_0 KV cache — both at a full 200K-token
context, measured with TurboLLM's auto-tuner. LM Studio on the same machine and models managed
61.0 and ~66 tokens/sec. Your exact figure depends on context length and settings, so TurboLLM
re-measures on your machine when you load the model.
Can the RTX 5070 Ti run a 70B model?
Not usably. A dense 70B GGUF at Q4_K_M is about 43 GB — nearly three times this
card's VRAM — and a dense model offloaded that heavily crawls. The better big-model path on
16 GB is a Mixture-of-Experts like Qwen3.6 35B-A3B: only ~3B parameters are
active per token, so it runs fast at Q3_K_M with a light expert nudge to
system RAM, or at Q4_K_M with fuller expert offload.
What quant should I use on 16 GB of VRAM?
For the 27B dense pick, IQ4_XS (15.4 GB) fits fully in VRAM; for the 35B-A3B MoE,
Q3_K_M (16.6 GB) runs with a light expert nudge to system RAM or
Q4_K_M (22 GB) with fuller expert offload.
Smaller 7–8B models can go near-lossless at Q8_0. TurboLLM shows a VRAM-fit
verdict for every quant against your card's real free VRAM before you download — the naming is
decoded in Quantization explained.
Do I need to install CUDA 13 for the RTX 5070 Ti?
No. Blackwell cards want an engine built against CUDA 13, but TurboLLM handles that
automatically: it detects the 5070 Ti and downloads a matching prebuilt
llama-server — no CUDA toolkit, no Python environment, no compiler. If you later
want a community fork like TurboQuant, the engines catalog handles
that too.
Where do the speed numbers on this page come from?
They were measured by TurboLLM's founder on this exact card — an RTX 5070 Ti 16 GB — running
TurboLLM and LM Studio head-to-head on the same machine, with the same model files, at a
200K-token context. Nothing on this page is extrapolated from another GPU or read off a spec
sheet. The full methodology is on the TurboLLM vs LM Studio
comparison page.
Run it on your RTX 5070 Ti
$npx turbollm
One command: it detects the 5070 Ti, provisions the Blackwell-native CUDA engine, and opens the
UI. Already have models in an LM Studio folder? TurboLLM indexes the .gguf files in
place — no re-download. New here? Start with
Install & first run and
Quantization explained. On different hardware, see the
RTX 5080 or RTX 5060 Ti guides.