Guide · 2026-08-29

Best Local LLMs for the RTX 5070 Ti (16GB) in 2026

Last updated 2026-08-29

16GB of GDDR7 and 896 GB/s of bandwidth make the RTX 5070 Ti the best price-per-token GPU below the RTX 5090. Here's what it actually runs.

By Mohamed Meguedmi · 6 min read

Why the RTX 5070 Ti Is the 16GB Sweet Spot

Nvidia's RTX 5070 Ti and RTX 5080 share the same GB203 Blackwell die and the same 16GB of GDDR7 — the 5070 Ti just has more compute units disabled. For gaming, that gap matters. For local LLM inference, it barely does. Token generation on a GPU is overwhelmingly bound by memory bandwidth, not compute, and the 5070 Ti's 896 GB/s is 93% of the 5080's 960 GB/s. The compute deficit costs you roughly 8-12% in real-world LLM throughput, not the ~25% price gap between the two cards ($749 vs. $999 MSRP).

That's the entire case for the 5070 Ti as a local-AI purchase: you get almost all of the 5080's memory performance, the full 16GB capacity, and you pocket the difference. If your budget is around $750-900 for a GPU and you have no interest in stepping up to a 5090, this is the card to buy.

RTX 5070 Ti Specs at a Glance

Spec	RTX 5070 Ti	RTX 5080	RTX 4070 Ti Super
Architecture	Blackwell (GB203)	Blackwell (GB203)	Ada Lovelace (AD103)
VRAM	16GB GDDR7	16GB GDDR7	16GB GDDR6X
Memory bandwidth	896 GB/s	960 GB/s	~672 GB/s
CUDA cores	8,960	10,752	8,448
Tensor Cores	5th-gen, native FP4	5th-gen, native FP4	4th-gen
TDP	300W	360W	285W
Recommended PSU	750W	850W	750W
Launch MSRP	$749	$999	$799

Street pricing has drifted since launch. Expect to pay $850-950 for a well-cooled custom AIB card from Asus, MSI, or Gigabyte in 2026, versus the original $749 Founders Edition MSRP. Check current listings on Newegg, Best Buy, or Amazon before assuming MSRP pricing is still available — Blackwell cards have held their value better than the previous generation.

What Actually Fits in 16GB

16GB is the line between "comfortable" and "tight" depending on model size and quantization. Here's the practical breakdown for the RTX 5070 Ti:

• 8B-9B models at Q8: Fully in VRAM with room to spare for a long context window. This is the sweet spot for daily-driver quality — Granite 4.2 8B and similar models leave 5-6GB of headroom for KV cache.
• 14B models at Q4: Fits entirely on-card. Third-party benchmarks have clocked 14B Q4 models in the neighborhood of 50 tok/s on this GPU, which is fast enough for interactive coding and chat use.
• 24B models (e.g., Mistral Small 3.1) at Q4: Fits, but with a tighter context budget — you'll want to trim the KV cache settings below to keep long conversations in VRAM.
• 70B-class models: Don't fit at any usable quant without offloading layers to system RAM, which tanks throughput. If 70B is the target, you need 24GB+ of VRAM.

For a full breakdown of which quantized models fit which VRAM tiers, the model catalog and the GPU-to-model configurator are the fastest way to check a specific model before you download tens of gigabytes of weights.

Setup: Drivers, CUDA, and Ollama

On Linux, getting a Blackwell card running local models is a five-minute job:

sudo apt update
sudo apt install nvidia-driver-570
sudo reboot

curl -fsSL https://ollama.com/install.sh | sh
ollama run mistral-small

On Windows, install the latest Studio or Game Ready driver from Nvidia (570-series or newer for full Blackwell support), then run the Ollama Windows installer — CUDA acceleration is detected automatically on both platforms. Either way, confirm you're on a driver branch that explicitly lists Blackwell/GB203 support before troubleshooting slow inference; an outdated driver is the most common cause of a 5070 Ti underperforming its specs.

Blackwell-Specific Optimizations Worth Knowing

A handful of settings meaningfully change what the 5070 Ti can do:

• Flash Attention 3: Enabled by default on Blackwell in recent llama.cpp and Ollama builds. It adds 10-14% throughput on prompts with 4k+ tokens of context — check your inference logs to confirm it's active.
• Q8 KV cache: Set OLLAMA_KV_CACHE_TYPE=q8_0 to quantize the key-value cache. On an 8B model like Granite 4.2, this shrinks a 32k-token context window from roughly 8GB down to 5-6GB, freeing VRAM for a bigger model or longer conversations.
• NVFP4 (future-proofing): The 5th-gen Tensor Cores support native FP4, but almost no inference stack fully exploits it yet as of 2026. When Ollama and llama.cpp ship mature FP4 kernels — likely within the next 12-18 months — expect throughput gains in the range of 40% on supported models, with no hardware upgrade required.
• Undervolting: Dropping the power target from 300W to roughly 250W (via MSI Afterburner, around -80mV) costs about 1% of LLM throughput while cutting temperatures by roughly 5°C and noticeably quieting the fans — a good trade for a card that will run inference jobs for hours at a time.

RTX 5070 Ti vs. the Alternatives

vs. RTX 5080

Same VRAM, 93% of the bandwidth, 8-12% less throughput, for about $250 less at MSRP. Unless you also game at 4K and want the extra compute, the 5080's price premium is hard to justify for LLM-only builds.

Upgrading from an RTX 4070 Ti Super?

You'd gain roughly 20% more LLM throughput and 33% more memory bandwidth (896 GB/s vs. ~672 GB/s). It's not a must-do if your current setup already runs the models you need, but it becomes an easy call if you can sell the 4070 Ti Super for $550-600 on the used market and put that toward the upgrade.

vs. a Used RTX 4090

A secondhand RTX 4090 (typically $1,800-2,000 now) still wins for 70B-class models thanks to its 24GB of VRAM. But if 70B isn't on your roadmap, the 5070 Ti delivers near-equivalent 14B-class performance for roughly half the money — a much better price-per-token outcome for most local-LLM users. Full head-to-head numbers across GPU generations are in the GPU comparison tool.

The Verdict

For anyone with roughly $750-1,000 to spend on a GPU who wants serious local LLM performance without stepping up to a 5090, the RTX 5070 Ti is the call. It beats the RTX 5080 on performance-per-dollar for AI workloads specifically, handles the 8B-24B range that covers most practical local use cases, and leaves real headroom for FP4 gains once software catches up to the hardware.

Before buying any GPU for local inference, it's worth cross-checking specs and quantized-model fit against real data rather than marketing slides — our benchmark methodology and the benchmarks hub are built for exactly that, and the underlying numbers are also available through our public API and open-source MCP server (CC BY 4.0) for anyone building their own hardware-selection tooling.