Best local LLMs for the RTX 5060 Ti (16 GB) — updated July 2026
The RTX 5060 Ti 16 GB is the cheapest new NVIDIA card that gets you 16 GB of VRAM — it launched
at a $429 MSRP, and that makes it the budget doorway into the 16 GB tier: a
27B dense model fits fully in VRAM at Q3–IQ4, and a
35B-A3B MoE becomes the daily driver, with even the huge Qwen3-Coder-Next
runnable via expert offload to system RAM. The honest caveats: its 448 GB/s of memory bandwidth
is exactly half an RTX 5070 Ti's, so it runs the same shortlist
noticeably slower — and a dense 70B is out of reach at any speed you'd actually use.
Numbers you can trust
TurboLLM measures real tokens/sec on your card and shows a
VRAM-fit verdict before you load — no guessed speeds in the app, ever.
Every speed on this page is labeled with its source: the only numbers we measured ourselves
came from a different card (an RTX 5070 Ti 16 GB), and everything quoted for the 5060 Ti is
community-reported.
Two cards share this name — this page is about the 16 GB one
The RTX 5060 Ti also ships as an 8 GB variant, and for local LLMs the VRAM is
the whole game: the 8 GB card can't hold this page's shortlist and maps closer to our
RTX 4060 (8 GB) guide. If you're buying for LLM work, the extra
VRAM is the single best money you can spend on this card.
The shortlist below is the models hub's 16 GB tier with the fit worked out for this card, plus the 8 GB tier's all-rounder promoted to a near-lossless quant. 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 — only ~3B params active per token, so it's dramatically faster than a dense model of similar quality and tolerates expert offload gracefully when VRAM gets tight.
RTX 5060 Ti:Q3_K_M (16.6 GB) just spills past 16 GB, so auto-fit nudges a few experts to system RAM — attention + KV stay on the GPU · or Q4_K_M (22 GB) + expert offload for higher quality · 256K context
The dense sibling — slower than the MoE but rock-solid quality, and the reason this card's 16 GB matters: it fits fully in VRAM at a mid quant, which the 8 GB variant simply cannot do.
RTX 5060 Ti:IQ4_XS (15.4 GB) is a tight fit — Q3_K_M (13.6 GB) leaves more KV-cache room · Q4_K_M (16.8 GB) + light offload · 256K context
The newest agentic coding MoE. Its experts live in system RAM while attention and KV stay on the GPU, so it runs even on this card — TurboLLM's auto-fit sets the split.
RTX 5060 Ti:IQ2–IQ3 with heavy CPU-expert offload (needs generous system RAM) · even better on 24 GB+ · large context
The speed pick for a 448 GB/s card — the 8 GB tier's all-rounder promoted to near-lossless Q8_0. It stays snappy where the 27B feels deliberate, with a toggleable thinking mode.
RTX 5060 Ti:Q8_0 (8.7 GB) fully in VRAM with headroom for a long context · or Q6_K (6.7 GB) for maximum context room · 128K context
The vision pick — current-gen vision-language model with strong OCR and spatial reasoning. A tiny footprint leaves lots of VRAM for images and context.
RTX 5060 Ti:Q4_K_M (5.0 GB) with plenty of headroom for images + context · or Q8_0 (8.7 GB) + mmproj (~0.8 GB) for max quality · 256K context
We haven't benchmarked this card ourselves, so the table below is what the community reports —
useful as a ballpark, not a promise. Generation speed is set almost entirely by the card's
448 GB/s of GDDR7 bandwidth (a healthy step up from the RTX 4060 Ti 16 GB's 288 GB/s):
Model benchmarked
Quant
Community-reported generation speed
DeepSeek-Coder 6.7B (dense)
Q4_K_M
~101 t/s
Mistral 7B (dense)
Q4_K_M
~90 t/s
Llama 2 13B (dense)
Q4_K_M
~53 t/s
Community-reported, runaihome.com's RTX 5060 Ti 16 GB Ollama benchmark (Ollama 0.23.2, Windows 11), May 2026 — all three ran fully in VRAM. Qwen3.6-generation numbers for this card aren't broadly published yet.
Those rows are last-generation 7–13B dense models; treat them as a bandwidth calibration, not a
forecast for the shortlist above. 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. Same 16 GB, same fits — but that card has
exactly twice the memory bandwidth (896 vs 448 GB/s), so don't map its number onto the 5060 Ti.
To be fair to the alternatives: LM Studio and Ollama run these same models on this card perfectly
well — what their model lists won't show you is a measured tokens/sec and a VRAM-fit verdict
for each model before you download — the part TurboLLM does differently.
How TurboLLM auto-fits the RTX 5060 Ti
Load any model and TurboLLM benchmarks it on your 5060 Ti, storing the measured
tokens/sec in the model list — live numbers from actual generation, not estimates.
Before you load, every quant gets a VRAM-fit verdict against your real 16 GB,
including KV-cache growth at your chosen context. Auto-fit then picks the GPU-layer count and
the MoE expert-offload split for you — it's what makes the Q3 35B-A3B above land correctly on
the first try. And because this is a Blackwell card, TurboLLM auto-provisions a
CUDA 13 engine build that supports it out of the box — no toolkit, no
compiling. Details in the auto-tune docs.
FAQ
Should I get the 8 GB or the 16 GB RTX 5060 Ti for local LLMs?
The 16 GB variant, without much debate. The 8 GB card saves money up front but halves the model
ceiling — for local LLM work it behaves like an RTX 4060, and our
RTX 4060 guide is the better map for it. The 16 GB variant is what
unlocks this page's shortlist: a 27B dense model fully in VRAM and a 35B-class MoE as the daily
driver.
Can the RTX 5060 Ti run a 70B model?
Not usably. A dense 70B at Q4_K_M is about 43 GB, so most of it would sit in system
RAM and generation drops to a few tokens per second. The smarter big-model path on 16 GB is a
Mixture-of-Experts: Qwen3.6 35B-A3B keeps only ~3B parameters active per token,
and even the much larger Qwen3-Coder-Next runs with its experts parked in system RAM.
What quant should I use on 16 GB of VRAM?
Q4_K_M is the best-quality-per-gigabyte default for models up to about 14B, and
they fit with plenty of context room. For a 27B dense, drop to IQ4_XS or
Q3_K_M to stay fully in VRAM; for the 35B-A3B MoE, Q3_K_M spills just
past 16 GB (auto-fit parks a few experts in system RAM) and Q4_K_M runs with expert
offload. TurboLLM shows a VRAM-fit verdict for
every quant before you download — the naming is decoded in
Quantization explained.
How fast will Qwen3.6 35B-A3B run on the RTX 5060 Ti?
We haven't measured this card, so we won't print a number. The only figure we've measured
ourselves comes from an RTX 5070 Ti 16 GB — 74.7 tokens/sec at a 200K
context — and that card has exactly twice the 5060 Ti's memory bandwidth, so don't map its
number across. TurboLLM benchmarks the model on your own card the moment you load it and shows
the real figure.
Do I need a special CUDA build for a Blackwell card like the RTX 5060 Ti?
You need one, but you don't have to make one. Blackwell GPUs require a CUDA build recent enough
to know the architecture — older binaries won't target them. TurboLLM detects the RTX 5060 Ti
and auto-provisions a CUDA 13llama-server build that supports
Blackwell out of the box: no CUDA toolkit, no compiling, no Python environment. If you later
want a community fork instead, the engines catalog handles that too.
Try it on your 5060 Ti
$npx turbollm
One command: it detects the 5060 Ti, provisions a Blackwell-ready CUDA 13 engine, and opens the
UI. New here? Start with Install & first run and
Quantization explained. Weighing this card against its
neighbors? The RTX 4060 (8 GB) is where the 8 GB variant lands, and
the RTX 5070 Ti (16 GB) runs this exact shortlist with double
the memory bandwidth.