Which LLM on RTX 4060 Ti (8 / 16 GB) ?

#RTX 4060 Ti for a local LLM: 8 GB or 16 GB

For a local LLM, get the 16 GB RTX 4060 Ti and skip the 8 GB version. Both versions use the same chip, 4,352 CUDA cores according to NVIDIA, and the same 288 GB/s memory bandwidth; only the capacity changes. That capacity determines what can be loaded. With 8 GB, you are limited to 4B to 9B models, such as Granite 4.2 8B or Qwen 3.5 9B. With 16 GB, Gemma 4 12B fits with real headroom for context, and gpt-oss 20B and Mistral Small 24B, each at 14 GB, fit in VRAM with little headroom. Speed does not change between versions: it is limited by the 128-bit bus. The used price of the 16 GB version should therefore be compared with that of a new 12 GB RTX 3060 or 16 GB RTX 5060 Ti.

Architecture

Ada Lovelace, 4,352 CUDA cores on both versions (product page NVIDIA).

Memory

8 GB or 16 GB of GDDR6, 128-bit bus, 288 GB/s bandwidth.

Power

160 W at 8 GB and 165 W at 16 GB according to Wikipedia; NVIDIA indicates a 550 W minimum power supply for the PC.

Launch price

$499 for the 16 GB version, released in July 2023 (source: Wikipedia).

Interface

PCIe 4.0 limited to 8 lanes, like the RTX 4060.

#8 GB or 16 GB: what each version can load

The table can be read using one rule: on 16 GB, keep at least 2 GB of headroom for the context cache and the system. That is comfortable with Gemma 4 12B, tight with gpt-oss 20B and Mistral Small 24B, and impossible with a 17 GB model. Ollama uses a 4,096-token context by default; beyond that, the K/V cache consumes this headroom, which is why quantizing it is worthwhile.

#Which model should you choose on the 16 GB version?

Gemma 4 12B

The versatile choice: 7.7 to 8.0 GB weights and a 256,000-token context announced in the Ollama library. You can extend the context to 16,000 tokens or more without spilling over.

gpt-oss 20B

Reasoning model, 14 GB. Usable in VRAM with a moderate context. See the dedicated installation guide.

Mistral Small 24B

14 GB for a dense model with 24 billion parameters: it reads more weights per token, so it is the slowest of the three on a card with 288 GB/s.

Qwen 3.5 9B

6.6 GB, the fallback model when you want a long context or multiple loaded models.

• gpt-oss locally with Ollama
• Which LLM for 16 GB of VRAM

#Install it and verify that everything is in VRAM

ollama run gemma4:12b
ollama run gpt-oss:20b
ollama run mistral-small
ollama ps

After every load, ollama ps should show 100% GPU. A split between CPU and GPU indicates an overflow into RAM, which severely slows generation on this card, whose PCIe link is limited to 8 lanes. On the 8 GB version, trying gemma4:12b or gpt-oss:20b produces exactly this situation.

#What speed to expect: ceilings and third-party measurements

No throughput is presented here as an in-house measurement. The theoretical generation ceiling is bandwidth divided by weight size. A public third-party benchmark (LLaMA 3 8B in Q4_K_M under llama.cpp, May 2024) observes 68% of the ceiling on one RTX 4080 and 75% on one RTX 4070 Ti, or roughly 70%. At 288 GB/s, this gives the following estimates, for a short context.

These figures follow the inverse logic of capacity: the larger the model, the more the bus matters. A mixture-of-experts model such as gpt-oss 20B reads only its active experts for each token, so its actual throughput therefore exceeds that of a dense model of the same size; no reliable measured value is included here. For chat, 14 tokens/s remains readable; for code with long outputs, it is slow.

#Manage context on 16 GB

The 2 GB margin on gpt-oss 20B or Mistral Small 24B disappears as the context grows. Ollama can quantize the K/V cache to q8_0, using about half the memory of the default f16; this requires Flash Attention to be enabled. On 16 GB, this setting turns a 14 GB model with a short context into one usable at 8,000 tokens or more. Set it when starting the server.

export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE=q8_0
ollama serve

• Quantize the KV cache to save VRAM

#4060 Ti 16 GB, 3060 12 GB, or 5060 Ti 16 GB

The choice comes down to two factors. If the target model weighs 14 GB, only a 16 GB card works, and the 5060 Ti 16 GB reads its memory 55% faster than the 4060 Ti, with a new-product warranty. If the target model fits in 12 GB, a used RTX 3060 12 GB is generally faster for less money. The 4060 Ti 16 GB is the right choice only if its used price is well below that of a new 5060 Ti: check it on the price-tracking page.

• Which LLM for RTX 5060 Ti
• Which LLM on RTX 3060 12 GB
• Local AI graphics card prices

#What 16 GB isn't enough to load

Sixteen gigabytes mark a clear threshold. Models of 14 GB fit, while those of 16 GB and more do not, even quantized in Q4, since room is still needed for the context. The table gives the sizes published by Ollama for models just above the threshold.

An overflowing model continues to respond, but some of its weights are read from system RAM over a PCIe 4.0 link limited to 8 lanes on this card. Generation becomes significantly slower than with a model that fits. If these models are your goal, or if you plan to keep the card for several years while models grow, the right decision is to target 24 GB at purchase rather than optimize a 16 GB card.

The narrow bus is not the only criterion. Prompt processing—that is, reading your question or documents before the first response—mainly exercises compute rather than memory. In this respect, the 4060 Ti benefits from its 4,352 CUDA cores, compared with 3,072 for the RTX 4060. A long document is therefore read relatively quickly (difference not measured here), even though the subsequent generation remains capped by 288 GB/s. This distinction explains why a card may feel responsive on the first word and then slow during continuous generation.

#Buy or wait: three questions to ask yourself

What is the largest useful model?

If it is a 14B to 24B model in Q4, 16 GB is the right capacity. If 8B to 9B is enough, an 8 GB card is already suitable, and the 16 GB version is then just an added cost.

Does speed matter?

For personal chat, 15 to 30 tokens/s is sufficient. For agents that generate long outputs in sequence, the 288 GB/s bandwidth becomes the limiting factor, and the 5060 Ti 16 GB is the clear choice.

New or used?

Buying used avoids the new price but comes without a warranty; a 2023 card may have been used for mining or intensive gaming. Check the temperatures and fan noise before buying.

#2026 verdict

Choose the 16 GB if

The used price is low and you are targeting 12B to 24B. You accept throughput limited by the bus.

Avoid the 8 GB version for LLMs

It loads no more than a RTX 4060 or a RTX 5060, which have the same capacity.

Prefer the 5060 Ti 16 GB

If you buy new, for the warranty and higher bandwidth.

• Source: official specifications NVIDIA (RTX 4060 Ti)
• Source: Gemma 4 sizes in the Ollama library
• Source: Ollama's official FAQ (Flash Attention, K/V cache)

#Frequently asked questions