Guide · 2026-08-31

Best LLMs to Run on the RTX 4060 Ti (8GB vs 16GB)

Last updated 2026-08-31

The 4060 Ti's 128-bit bus starves it for bandwidth on LLM workloads — here's which VRAM config and models actually make sense in 2026.

By Mohamed Meguedmi · 6 min read

RTX 4060 Ti Specs: Where the Bottleneck Really Is

Nvidia sells the RTX 4060 Ti in two VRAM configurations, and it's easy to assume the 16GB card is simply a faster version of the 8GB one. It isn't. Both use the same Ada Lovelace AD106 die built on TSMC's 4N process, with an identical 4,352 CUDA cores. The only difference is memory capacity — and that distinction matters enormously for anyone running local LLMs, but not in the way most buyers expect.

Spec	RTX 4060 Ti 8GB	RTX 4060 Ti 16GB
Architecture	Ada Lovelace, AD106 (TSMC 4N)	Ada Lovelace, AD106 (TSMC 4N)
CUDA cores	4,352	4,352
VRAM	8GB GDDR6, 18 Gbps	16GB GDDR6, 18 Gbps
Memory bus	128-bit	128-bit
Memory bandwidth	288 GB/s	288 GB/s
TDP	160W	165W
Recommended PSU	550W	550W
Typical used price (2026)	~$260	~$400-430

That 288 GB/s bandwidth figure is the headline problem. For gaming, a 128-bit bus with a large L2 cache is a reasonable trade-off. For LLM inference, it's a serious handicap. Every token a model generates requires streaming its weights through VRAM, so throughput scales almost directly with memory bandwidth, not raw compute. A card with plenty of CUDA cores but a starved memory bus ends up compute-idle, waiting on data.

8GB vs 16GB: Same Bottleneck, Different Ceiling

Because both variants share the identical 288 GB/s bus, the 16GB card is not faster than the 8GB card for the models that fit on both — it's exactly as memory-bound. What 16GB buys you is headroom: the ability to load a 12B, 20B, or even a quantized 24B model that simply won't fit in 8GB at usable quantization, plus room for longer context windows without spilling into system RAM.

The comparison that actually matters is against the competition, not between the two 4060 Ti variants. At equal VRAM, the RTX 4060 Ti runs roughly 20-30% slower than a used RTX 3060 12GB in token generation — despite the 3060 typically costing less. That's a direct consequence of bus width: the 3060 12GB uses a 192-bit bus for meaningfully higher bandwidth, and in a bandwidth-bound workload like LLM inference, that gap shows up directly in tokens per second. If you're shopping by VRAM capacity alone, you can end up with a slower, pricier card.

This is why we treat the 4060 Ti as a capacity play, not a speed play. It's worth buying specifically for the 16GB configuration and the model sizes it unlocks — not because it's fast.

Which Models Actually Fit and Run Well

Given the bandwidth ceiling, model selection should be driven by VRAM headroom first and quantization second. Here's what we'd load on each configuration.

8GB configuration

• Qwen 3.5 9B (Q4) — fits with headroom for a moderate context window.
• Granite 4.2 8B — IBM's efficient general-purpose model, a solid 8GB fit.
• Qwen 3.5 4B — fast, comfortable margin for longer conversations.
• Granite 4.2 3B — the safest pick if you want to run multiple models or keep other apps in VRAM simultaneously.

16GB configuration

The 16GB card runs everything above plus a genuinely useful step up in capability:

• Gemma 4 12B — Google's mid-size model, a strong daily-driver fit at this VRAM tier.
• Qwen 3.5 9B (Q8) — the same model as the 8GB list, but at full 8-bit precision instead of Q4, for noticeably better output quality.
• gpt-oss 20B — the largest practical model for this card; expect it to use nearly all 16GB with a modest context window.
• Mistral Small 24B (Q4) — fits at aggressive quantization; treat it as a capability ceiling rather than a comfortable everyday choice.

None of this changes the underlying throughput story — a 288 GB/s card generating tokens from a 20B model will feel noticeably slower than the same card running a 9B model. The 16GB variant expands what you can run, not how fast you run it.

Setting Up Ollama on Windows

Getting a model running takes minutes. Install Ollama, then pull whichever model matches your VRAM tier:

winget install Ollama.Ollama
ollama run qwen3.5:9b   # fits comfortably on 8GB

# 16GB card:
ollama run gemma4:12b
ollama run gpt-oss:20b
ollama run mistral-small

Ollama auto-detects your GPU and offloads as many layers as VRAM allows; if a model doesn't fully fit, it'll spill remaining layers to system RAM, which tanks throughput. If you see generation slow dramatically partway through a chat, that's usually context length pushing you past your VRAM budget rather than a driver issue. For a broader rundown of which quantization level to pick for a given card, see our guides hub.

Buying Guide: 8GB, 16GB, or Wait for the 5060 Ti?

Here's how we'd spend a GPU budget built around this card in 2026.

Used RTX 4060 Ti 16GB at ~$400-430: buy it

This is the strongest case for the card. At this price, it's an excellent entry point for running genuinely capable 12B-20B models — Gemma 4 12B, gpt-oss 20B, Mistral Small 24B — without spending $600+ on a used RTX 3090. Among 16GB cards under $500, this is currently the best value we'd recommend.

Used RTX 4060 Ti 8GB: skip it

Throughput is fine for what it runs, but the 7B-8B ceiling is limiting, and you're not saving enough over the 16GB variant to justify the cut. A non-Ti RTX 4060, or a used RTX 3060 12GB, gets you more usable VRAM at a comparable or lower price. Check current listings against our GPU comparison tool before committing.

New RTX 5060 Ti 16GB at ~$500: prefer it if budget allows

If you can stretch roughly $100 past the used 4060 Ti 16GB price, the 5060 Ti is the better buy outright — about 40% faster, backed by a full manufacturer warranty instead of a used-market gamble, and it adds FP4 support that newer quantized model releases are starting to target. At near-identical pricing, it makes the 4060 Ti a harder sell for anyone buying new rather than used.

Whichever card you land on, run the numbers through our cost calculator to compare total cost against cloud inference for your expected usage, or use the build configurator to see how the 4060 Ti pairs with the rest of a budget local-LLM rig.

How We Evaluate GPUs for Local LLMs

Our verdicts on cards like the 4060 Ti come from weighing memory bandwidth, VRAM capacity, and real-world model fit together rather than any single spec in isolation — bandwidth-bound workloads like LLM inference punish narrow-bus cards regardless of how much VRAM or CUDA horsepower they carry. We publish the full scoring approach on our methodology page, and the underlying model-compatibility data that powers picks like this one is also available through the BestLLMfor public API (CC BY 4.0) and our open-source MCP server, so you can pull the same VRAM-fit and bandwidth data into your own tooling.