Which LLM on RTX 5060 (8 GB) ?

#RTX 5060 for a local LLM: what 8 GB of GDDR7 enables

A RTX 5060 runs 3- to 9-billion-parameter models in Q4 without difficulty. Qwen 3.5 4B weighs 3.4 GB, Granite 4.2 8B 5.3 GB, and Qwen 3.5 9B 6.6 GB according to the Ollama library: all fit within the card’s 8 GB, with the last requiring a short context. A 12B model, such as Gemma 4 12B (7.7 to 8 GB), already occupies all the memory before the first context token. The card’s strength is its memory bandwidth: 448 GB/s on a 128-bit bus, versus 272 GB/s for the RTX 4060, or 65% more. For chat, summarization, or a small RAG system, the 5060 is very comfortable. To go beyond 9B, you need more VRAM, not more speed.

Architecture

Blackwell, 3,840 CUDA cores, and 5th-generation Tensor Cores according to NVIDIA.

Memory

8 GB of GDDR7 on a 128-bit bus, with 448 GB/s of bandwidth.

Power

145 W of total graphics power; NVIDIA specifies a 550 W minimum power supply for the entire PC.

Launch price

$299, released on May 19, 2025, according to Wikipedia.

#Why the 5060 is faster than the 4060 at the same capacity

During generation, each token forces the GPU to reread most of the model's weights. Maximum speed is therefore bandwidth divided by weight size. The 5060 reads its memory 65% faster than the 4060: with the same model, the theoretical ceiling increases by the same proportion. This is the only technical argument that matters for an LLM. CUDA cores help read the prompt, not generation, which remains memory-bound.

#Which models fit in 8 GB

Gemma 4 12B illustrates the file-size trap: its weights take up 7.7 to 8 GB, but the card must also accommodate the context cache and display. The model loads, but with a context of only a few thousand tokens at best, and it spills into RAM as soon as an application uses a little VRAM. On 8 GB, Qwen 3.5 9B or Granite 4.2 8B are safer choices and leave usable headroom.

#Images, vision, and quantization: two clarifications

The Ollama library indicates that Qwen 3.5 accepts text and images: a 4B or 9B model can therefore describe a screenshot or read a diagram. Images consume context, reducing headroom on 8 GB; prefer the 4B (3,4 GB) for vision if you want to preserve space. For quantization, Q4_K_M remains the right compromise on 8 GB: moving to finer quantization adds several hundred megabytes to the weights—headroom the card does not have—with no visible change for everyday use.

#Install Ollama and check the card

1. 01
   NVIDIA driver

   Ollama requires NVIDIA driver 550 or later. For an RTX 50, install the latest driver offered by NVIDIA. Check with nvidia-smi.
2. 02
   Install Ollama

   The RTX 5060 appears on the list of supported cards (compute capability 12.0). CUDA is built in, so no separate installation is required.
3. 03
   First model

   Run ollama run qwen3.5:9b (6.6 GB) or ollama run granite4.2:8b (5.3 GB) if you prefer to keep some headroom.
4. 04
   Control

   Run ollama ps: the PROCESSOR column must display 100% GPU.

#What speed to expect: a ceiling, not a measurement

No throughput figure presented here is an in-house measurement. The generation ceiling is bandwidth divided by weight size. A public third-party measurement (LLaMA 3 8B in Q4_K_M under llama.cpp, May 2024) records 106 tokens/s on a RTX 4080 with a ceiling of 156 tokens/s, or about 68%. Using 70% as a rough benchmark, we get the following estimates for a short context.

All these ceilings are well above the comfortable reading threshold, around 15 to 20 tokens/s. Speed therefore won’t limit the 5060: its 8 GB capacity will.

#Set Ollama so it stays within 8 GB

Ollama quantizes the K/V cache to q8_0, using about half the memory of the default f16, with very little loss of precision according to its documentation; this requires Flash Attention. On 8 GB, it is the most cost-effective lever: it lets you extend the context without changing models.

export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE=q8_0
export OLLAMA_CONTEXT_LENGTH=8192
ollama serve

A single model

Load only one model at a time; an embedding model for a RAG must remain small.

Resource-hungry applications

Browsers with hardware acceleration, games, and video encoding reserve VRAM: close them before loading the model.

Suitable context

Every doubling of the context doubles the K/V cache: increase it only for tasks that require it.

Monitoring

nvidia-smi during a long generation shows the actual headroom.

• Quantize the KV cache to save VRAM
• Which LLM for 8 GB of VRAM?

#What you can actually do with 8 GB

The right criterion is the required workload, not the model's size. Chat and rephrasing work on a 4B model just as they do on an 8B model. Summarizing documents of a few dozen pages requires a context of 8 000 to 16 000 tokens: with the K/V cache in q8_0, an 8B model can handle it on 8 GB. A RAG combines a generation model, an embedding model, and the context of the retrieved excerpts: keep the generation model below 9B. The coding assistant is the most demanding case, because specialized models quickly exceed the card's capacity.

If a model spills over, generation does not stop: some weights are read from system RAM. The symptom is a sharp drop in throughput. The ollama ps command shows the split between GPU and CPU; a line at 100% GPU is healthy, while a shared line indicates spillover, which you can fix by reducing the context or changing models.

#When the 5060 is no longer enough

Three signals indicate that you need more memory. You want a 12B or larger model for high-quality writing. Your context regularly exceeds 16 000 tokens because of long documents. You run multiple models together: generation, embeddings, and a reranker. In these cases, adding speed changes nothing: capacity is what you lack, and the next tiers are described on the 12 GB and 16 GB pages.

• Which LLM for 12 GB of VRAM
• Which LLM for 16 GB of VRAM

#5060 or 5060 Ti 16 GB: the tradeoff that matters

The RTX 5060 Ti 16 GB uses the same GDDR7 memory on a 128-bit bus, and therefore the same 448 GB/s bandwidth according to Wikipedia. Its 4,608 CUDA cores and 16 GB of memory are its real differences. It therefore generates at the same speed on a model that fits on both cards, but it can also load 14 GB models that the 5060 cannot fit. The launch MSRP was 299 dollars for the 5060, 379 dollars for the 5060 Ti 8 GB, and 429 dollars for the 5060 Ti 16 GB: the 130-dollar difference buys twice the capacity.

• Which LLM for RTX 5060 Ti
• Local AI graphics card prices

#2026 verdict

Buy a 5060 if

Your models range from 4B to 9B, your budget is tight, and you want something new with a warranty. It’s the fastest 8 GB card.

Prefer the 5060 Ti 16 GB if

You are targeting 12B to 24B: capacity takes priority over speed, and the extra cost is modest.

Avoid the 5060 if

You plan to use Gemma 4 12B, gpt-oss 20B, or any model over 8 GB seriously.

The summary in one sentence: the 5060 is the 8 GB card you choose for its speed and price, not its capacity. If your use case fits within 8 GB, it is hard to beat new; if it does not, no setting will make up for the missing 8 GB, and the next card in the lineup is the right investment.

• Source: official NVIDIA specifications (RTX 5060 and 5060 Ti)
• Source: GPU NVIDIA supported by Ollama
• Source: Ollama's official FAQ (Flash Attention, K/V cache)

#Frequently asked questions