Which LLM on RTX 5060 Ti (8 / 16 GB) ?

#RTX 5060 Ti for a local LLM: what 16 GB at 448 GB/s enables

For a local LLM, a RTX 5060 Ti 16 GB keeps models up to 14 to 20 billion parameters entirely in VRAM, but its 448 GB/s bandwidth makes it two to three times slower than high-end cards. According to the Ollama library, Qwen3 14B weighs 9.3 GB and gpt-oss 20B 14 GB. Hardware Corner measures 32.9 tokens per second on Qwen3 14B with 16,000 tokens of context, and 43.8 on gpt-oss 20B with 128,000 tokens. Models with 27 to 32 billion parameters, at 17 GB and above, do not fit. The 8 GB version, meanwhile, is limited to models with 9 billion parameters or fewer.

Memory

16 GB or 8 GB of GDDR7 on a 128-bit bus, according to NVIDIA; 448 GB/s of bandwidth, according to Hardware Corner.

Power consumption

180 W of graphics power and 600 W of required system power, according to NVIDIA. This exceeds the 550 W figure that is often cited.

Software

Compute capability 12.0, supported by Ollama with driver 550 or later.

#8 GB or 16 GB: the only question that really matters

The two versions share the GPU and memory bandwidth: only capacity changes, and that determines what you can run. NVIDIA offers the 5060 Ti with 16 GB and 8 GB; the comparison below uses the Ollama library files accessed on September 29, 2026.

With 8 GB, a 9-billion-parameter model leaves 1.4 GB for context and the system: that's tight, and spilling into RAM, which is slower, happens quickly. The 16 GB version doubles the catalog and opens the door to 12- to 20-billion-parameter models. For regular LLM use, the 16 GB version is the only one that retains headroom, even though it remains well behind a 24 GB card.

#Calculate your headroom before downloading a model

The rule is simple: card capacity minus file size gives you the raw headroom, and that headroom must cover the KV cache, engine buffers, and what the operating system uses for display. With Qwen3 14B, 16 - 9.3 leaves 6.7 GB; with Qwen 3.5 9B on the 8 GB version, 8 - 6.6 leaves 1.4 GB. The thinner the margin, the more you need to reduce the context and quantize the cache. If ollama ps shows a split between CPU and GPU, the model or its context is spilling over: generation then drops toward RAM speed.

On the 8 GB version, three habits prevent most overflows. Choose a model with 9 billion parameters or fewer, whose file stays under 7 GB. Keep the context modest as long as ollama ps shows 100% GPU. And close applications that consume VRAM, such as the browser or games, before launching the model.

• Which LLM for 8 GB of VRAM: models for the 8 GB version

#Models to prioritize on 16 GB

The best approach is to start with Qwen3 14B or gpt-oss 20B. Qwen3 14B leaves nearly 7 GB for context. gpt-oss 20B fits in 14 GB because, according to Ollama, its expert weights are quantized in MXFP4 at 4,25 bits per parameter, allowing it to run on 16 GB of memory. A 24-billion-parameter model in Q4, such as Devstral Small 2 (15 GB), leaves only one GB: it fits in a short exchange, not on an agent. A local RAG adds an embedding model: bge-m3 weighs 1,2 GB in Ollama, for a total of 7,8 GB with Qwen 3.5 9B.

• Which LLM for 16 GB of VRAM: the complete list, across all GPUs

#Install Ollama on a RTX 5060 Ti

1. 01
   Check the driver

   Run nvidia-smi in a terminal: the driver must be version 550 or later (551.61 on Windows), and total memory must show 16 GB, or 8 GB for the other version.
2. 02
   Install Ollama

   Install Ollama from its official website. The local API listens on http://localhost:11434.
3. 03
   Run a model

   Run ollama run qwen3:14b on 16 GB, or ollama run qwen3.5:9b on 8 GB. You only need to download it once.
4. 04
   Control the distribution

   In a second terminal, run ollama ps: the Processor column should show 100% GPU. On 8 GB, a CPU/GPU split means the model or context overflows: choose a lighter model.
5. 05
   Adjust the context

   Set the context with OLLAMA_CONTEXT_LENGTH, then run ollama ps again. If there isn't enough headroom, set OLLAMA_FLASH_ATTENTION to 1 and OLLAMA_KV_CACHE_TYPE to q8_0 at startup.

ollama run qwen3:14b
ollama run gpt-oss:20b
ollama ps

# Exemple de la documentation Ollama : contexte de 64 000 tokens
OLLAMA_CONTEXT_LENGTH=64000 ollama serve

• Install Ollama on Windows 11: complete guide

#Measured throughput: bandwidth sets the pace

The figures come from Hardware Corner, which tests the 16 GB version on llama.cpp with contexts from 4k to 128k. These are not QuelLLM measurements.

Generation is limited by bandwidth: the theoretical ceiling is approximately bandwidth divided by model weight. For Qwen3 14B (9.3 GB), 448 ÷ 9.3 gives 48 tokens per second, and the 4k measurement, 41.1, reaches 85% of that. Context costs speed: Qwen3 14B loses 37% between 4k and 32k (41.1 to 25.9). gpt-oss 20B, a mixture-of-experts model, exceeds its apparent ceiling (448 ÷ 14 = 32) at 92.1 tokens per second because it reads only part of its weights per token.

Ollama sets the default context based on VRAM: its documentation specifies 4k tokens under 24 GiB of VRAM and recommends at least 64,000 tokens for agents and web research. The 5060 Ti falls into the 4k tier. According to its FAQ, q8_0 reduces the KV cache to about half the memory used by f16, with very little precision loss: it is the first setting to enable on 16 GB, and essential on 8 GB.

• Quantize the KV cache to save VRAM in long contexts

#5060 Ti versus other cards: the measured gap

This table shows the 5060 Ti’s tradeoff: it generates nearly half again as fast as the 4060 Ti 16 GB (100 ÷ 68), thanks to its GDDR7 memory (448 versus 288 GB/s), but it is still far behind the 5070 Ti, which offers the same capacity with 76% more speed. For throughput alone, a 12 GB 4070 Super is faster, but it cannot fit gpt-oss 20B. According to Hardware Corner, its price-to-memory ratio makes it the beginner “value king” in 2026; compare it with current price tracking.

Hardware Corner also notes that two 16 GB cards can provide 32 GB without the cost of a flagship card. Splitting the workload across two GPUs uses llama.cpp and its tensor-split mode, detailed in the multi-GPU guide; it adds capacity, not per-card speed.

• Multi-GPU LLM with llama.cpp: tensor-split across 2 cards

#Verdict: 16 GB, 8 GB, or another card

A decision method comes down to two questions. What is the largest model you want to run? Under 9 billion, the 8 GB version is enough; between 12 and 20 billion, you need the 16 GB version; beyond that, you need a 24 GB card or two cards. What speed are you willing to accept? At 40 tokens per second on a 14B, reading remains smooth; the 5070 Ti is justified only if you generate long texts or serve multiple people.

For fine-tuning, Unsloth lists the following minimum VRAM requirements for 4-bit QLoRA: 6 GB for an 8-billion-parameter model, 8.5 GB for 14 billion, and 22 GB for 27 billion. The 16 GB version can therefore fine-tune a 14B model, with a batch size of 1 and a short context. For today's prices, check our tracker, which records the lowest price for each card twice a week.

• Graphics card prices for local AI (updated twice a week)

• Source: NVIDIA, RTX 5060 family specifications
• Source: Hardware Corner, LLM benchmarks on RTX 5060 Ti 16 GB
• Source: Ollama, gpt-oss model page
• Source: Ollama documentation, context length
• Source: Unsloth, VRAM required for fine-tuning

#Frequently asked questions