Which LLM on RTX 5050 (8 GB) ?

#RTX 5050 for a local LLM: what 8 GB of GDDR6 can handle

A RTX 5050 can comfortably run 3- to 9-billion-parameter models in Q4. 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: they fit within the card’s 8 GB, with the last one limited to a short context. A 12B model in Q4 uses about 7 to 8 GB, leaving no room for context. The speed limit comes from memory: the 5050 uses GDDR6 on a 128-bit bus, delivering 320 GB/s according to Wikipedia, while the RTX 5060 uses GDDR7 at 448 GB/s. That is sufficient for exploring local LLMs and using them for simple tasks. For demanding daily use, the 8 GB capacity quickly becomes the limiting factor.

Architecture

Blackwell, 2 560 CUDA cores and 5th-generation Tensor Cores, 421 AI TOPS according to NVIDIA.

Memory

8 GB of GDDR6 on a 128-bit bus, with 320 GB/s of bandwidth.

Power

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

Launch price

249 dollars, released on July 1, 2025 according to Wikipedia.

#GDDR6 vs. GDDR7: why bandwidth makes the difference

In text generation, the GPU rereads most of the model's weights for each token produced. Maximum speed is therefore bandwidth divided by weight size. The 5050 is not slower than its predecessors because of its cores, but because of its memory: 320 GB/s versus 448 GB/s for the RTX 5060, meaning the latter has 40% more at the same capacity. The 5050 is nevertheless faster than a RTX 4060 (272 GB/s), and it adds fifth-generation Tensor Cores.

This comparison clarifies the choice among the three cards. If your models fit within 8 GB, the 5060 is clearly faster and the 5050 is the price compromise. If your models exceed 8 GB, no 8 GB card is suitable, and the used 3060 12 GB is the obvious alternative.

#Which models fit in 8 GB

The site's rule of thumb is about 0.6 GB per billion parameters in Q4, weights only. Ollama uses a 4,096-token context by default; beyond that, the K/V cache grows and eats into the headroom. Less than 1.5 GB of headroom is a warning sign, because the display system and other programs also take their share of the card: even the smallest program using VRAM can then spill the model into system RAM.

#Install Ollama on a RTX 5050

1. 01
   Update the driver

   Ollama requires a NVIDIA 550 driver or newer. Install the latest driver available for the RTX 50: it is required for Blackwell generation.
2. 02
   Install Ollama

   Download the installer for your system; CUDA is bundled.
3. 03
   Run a model

   Try ollama run qwen3.5:4b for a smooth first experience, then ollama run granite4.2:8b for higher quality.
4. 04
   Check placement

   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. We calculate the generation ceiling by dividing bandwidth by weight size. A public third-party measurement (LLaMA 3 8B in Q4_K_M under llama.cpp, May 2024) reports, for the RTX 4080, 106 tokens/s against a ceiling of 156 tokens/s, or approximately 68%. Using 70% as a rule of thumb gives the following estimates for a short context.

A comfortable reading speed starts at around 15 to 20 tokens/s; all these models exceed it. Estimates decrease with context, and reading the prompt weighs more heavily on compute than on memory.

#Get the most out of 8 GB

Three settings matter. The quantized K/V cache in q8_0 uses about half the memory of f16, the default, provided you enable Flash Attention. A context suited to the use case avoids wasting VRAM. Finally, loading only one model at a time is the rule on 8 GB, regardless of the size of the models involved.

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

A single model loaded

Ollama keeps a model in memory after use; if you switch models, check with ollama ps that the previous one has been unloaded, otherwise the two compete for the 8 GB.

On-demand context

Increase the context only for tasks that require it: each doubling doubles the K/V cache.

Graphics applications

Browsers, games, and video-editing software reserve VRAM. Close them before loading and check with nvidia-smi.

Quantization

Stick with Q4_K_M: an 8B model in Q8 weighs almost 8 GB by itself and exceeds the card's capacity.

These settings don't increase the card's capacity; they prevent wasting it. If a model still exceeds it, generation does not stop: some of the weights are read from system RAM, which slows it down significantly. The Ollama documentation describes this case in the ollama ps output, with a column showing the split between GPU and CPU.

• Quantize the KV cache to save VRAM
• Troubleshoot Ollama: GPU not detected, slowdowns, out-of-memory errors

One useful clarification: laptops also carry the name RTX 5050. Their specifications and power limits depend on the manufacturer, and this guide covers the desktop card. Check your machine's specifications, then the memory actually available, before applying the bandwidth figures above.

#What you can actually do with 8 GB

The right criterion isn't model size but the work you need to do. A chat, a summary of a few pages, or a rewrite can be handled by a 4B to 8B model without difficulty. RAG over your documents requires a generation model and an embeddings model, plus enough context for the excerpts: with 8 GB, keep the generation model to 4B or 8B. A coding assistant needs more context and often a larger model, which becomes restrictive. For vision or agents that chain tools together, 8 GB fills up quickly.

#When the 5050 is no longer enough

Three signals indicate that you need more memory. You want to load a 12B model or larger, such as a higher-quality model for writing. Your context regularly exceeds 16 000 tokens because you work with long documents. You load several models at once, such as a generator, embeddings model, and reranker. In all three cases, adding speed solves nothing: capacity is what you lack. The dedicated 12 GB and 16 GB pages describe the next tiers, and the VRAM calculator lets you check a specific model before buying.

• Which LLM for 8 GB of VRAM?

#RTX 5050, RTX 5060, or RTX 3060 12 GB: which one to choose

The choice depends on the size of the models you target. For 4B to 8B models, the 5060 offers 40% more bandwidth for $50 more in suggested retail price ($299 versus $249 at launch), which is a good value. For 9B to 14B models, neither is suitable: a used 12 GB 3060 supports models that 8 GB cards cannot. Check the price tracker to compare cards when you buy.

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

#2026 verdict

Buy a 5050 if

You want something new with a warranty, modest usage (chat, summarization, small RAG), and a tight budget.

Prefer the 5060

If the budget allows: the same capacity, with 40% more bandwidth.

Prefer a used 3060 12 GB

If your models exceed 8 GB, capacity—not speed—is what you will run short of.

• Source: official NVIDIA specifications (RTX 5050)
• Source: GPU NVIDIA supported by Ollama
• Source: Granite 4.2 sizes in the Ollama library

#Frequently asked questions