CanMyPCRunAILocal AI compatibility

NVIDIA · 8GB VRAM

What AI models can a GeForce RTX 5060 run?

A GeForce RTX 5060 has 8GB of VRAM. Checked against 225 models from the Ollama library, 158 have good/excellent reference fits on the GPU at 4K context and 44 more run with part of the model in system RAM.

158

run well

44

with trade-offs

225

builds evaluated

Check my actual PC

Which GeForce RTX 5060 does this cover?

The desktop RTX 5060 keeps 8 GB of memory. Start with a compact build and check context growth; GDDR7 does not turn this into a 16 GB card.

Architecture
Blackwell
Memory
8 GB GDDR7
Interface
128-bit
Memory bandwidth
Not verified here

Ollama acceleration by operating system

  • Windows: vulkan, cuda when the corresponding driver/backend is installed.
  • Linux: vulkan, cuda when the corresponding driver/backend is installed.
  • Ollama’s GPU overview specifies driver 550 or newer; its Windows guide specifies 551.61 or newer. New cards can require a later driver/runtime. The reference has no measured driver version.
  • Confirm the loaded model's processor allocation with ollama ps. Intel Macs use the Ollama CPU path; Apple Silicon uses its integrated Metal GPU.

Hardware specifications · Ollama support table · reviewed 2026-09-15.

Compare GeForce RTX 4060 (8 GB). The same capacity can give the same fit result; runtime support and measured speed are separate questions.

Estimate this card on LinuxCompare memory budgetsWhy equal VRAM is not equal speed

Four builds to compare by task

Editorial examples chosen for their documented tasks, all Q4_K_M at 4,096 tokens. Check the verdict before downloading. These are estimates, not benchmark rankings.

Short conversations and summaries

llama3.2:3b

A small instruction-tuned model for trying local chat with more memory headroom. Review summaries against the source text.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 2.9 GB · medium confidence

Model fully fits in usable GPU VRAM.

ollama run llama3.2:3b

General instruction following

llama3.1:8b

An 8B instruction-tuned alternative to compare on your own prompts. It needs more memory than the 3B example and can still give incorrect answers.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 5.8 GB · medium confidence

Model fully fits in usable GPU VRAM.

ollama run llama3.1:8b

Code explanation and editing

qwen2.5-coder:7b

A code-focused instruct build. Review generated changes and run your tests; a memory fit is not evidence that its code is correct.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 5.3 GB · medium confidence

Model fully fits in usable GPU VRAM.

ollama run qwen2.5-coder:7b

A larger general-purpose comparison

qwen2.5:14b

A 14B instruct build for comparing answers when memory allows. Larger size does not establish better results for every task.

Task and build source · Catalog checked 2026-10-13

Runs with trade-offs

Est. memory 10.2 GB · medium confidence

Estimated 69% of layers offloaded to GPU (33/48); model does not fully fit in VRAM.

ollama run qwen2.5:14b

After opening Ollama's prompt, enter /set parameter num_ctx 4096 to match this calculation, within the model's supported maximum. Install a current Ollama release that supports the build. Setup and troubleshooting.

Models that fit — largest memory first — for a GeForce RTX 5060

Largest first — these have good/excellent reference fits with 8GB of VRAM at 4K context. A good fit can include a small estimated CPU offload. These are technical defaults, not a quality ranking.

Models that run well on a GeForce RTX 5060
ModelBuildMemory neededVerdict
nous-hermes2Ollama Librarynous-hermes2:10.7b-solar-q4_K_MQ4_K_M~7.6 GBGood fit
solarUpstagesolar:10.7b-text-v1-q4_K_MQ4_K_M~7.6 GBGood fit
deepseek-ocrDeepSeek AIdeepseek-ocr:3bF16~7.4 GBGood fit
olmo2Allen Institute for AIolmo2:7bQ4_K_M~6.9 GBGood fit
olmo-3Allen Institute for AIolmo-3:7b-instruct-q4_K_MQ4_K_M~6.9 GBGood fit
codegeex4Zhipu AIcodegeex4:9b-all-q4_K_MQ4_K_M~6.8 GBGood fit
glm4Zhipu AIglm4:9b-text-q4_K_MQ4_K_M~6.8 GBGood fit
nimble-v3Ollama Librarynimble-v3:9b-q4_K_MQ4_K_M~6.6 GBGood fit
ornith-1.5Ollama Libraryornith-1.5:9bQ4_K_M~6.6 GBGood fit
nimbleOllama Librarynimble:9b-q4_K_MQ4_K_M~6.6 GBGood fit
ornithOllama Libraryornith:9bQ4_K_M~6.6 GBGood fit
nemotron-3-nanoNVIDIAnemotron-3-nano:4bQ4_K_M~6.5 GBGood fit
deepseek-llmDeepSeek AIdeepseek-llm:7b-base-q4_K_MQ4_K_M~6.5 GBGood fit
llama2Meta AIllama2:7b-text-q4_K_MQ4_K_M~6.5 GBGood fit
duckdb-nsqlOllama Libraryduckdb-nsql:7b-q4_K_MQ4_K_M~6.5 GBGood fit
orca2Ollama Libraryorca2:7b-q4_K_MQ4_K_M~6.5 GBGood fit
wizardcoderOllama Librarywizardcoder:7b-python-q4_K_MQ4_K_M~6.5 GBGood fit
wizard-mathOllama Librarywizard-math:7b-q4_K_MQ4_K_MMemory scenario exceeds documented maximum of 2,048 tokens.~6.5 GBGood fit
wizardlmOllama Librarywizardlm:7b-q4_K_MQ4_K_MMemory scenario exceeds documented maximum of 2,048 tokens.~6.5 GBGood fit
magicoderOllama Librarymagicoder:7b-s-cl-q4_K_MQ4_K_M~6.5 GBGood fit
llavaOllama Libraryllava:7b-v1.5-q4_K_MQ4_K_M~6.5 GBGood fit
codellamaMeta AIcodellama:7b-python-q4_K_MQ4_K_M~6.5 GBGood fit
meditronOllama Librarymeditron:7b-q4_K_MQ4_K_MMemory scenario exceeds documented maximum of 2,048 tokens.~6.5 GBGood fit
orca-miniOllama Libraryorca-mini:7b-v2-q4_K_MQ4_K_MMemory scenario exceeds documented maximum of 2,048 tokens.~6.5 GBGood fit
llama2-chineseMeta AIllama2-chinese:7b-chat-q4_K_MQ4_K_M~6.5 GBGood fit

Showing the 25 largest of 158 models that run well.

How these results were calculated

Each model is evaluated at 4K context against 8GB of VRAM, counting model weights, KV cache, compute buffers and runtime overhead, minus a reserve for the display and operating system. Download sizes come from the official Ollama registry.

Reference scenario: Windows 11, 32GB of system RAM, an illustrative 8-core/16-thread CPU and 500GB free disk. Available RAM, driver version and free VRAM are unknown; the engine applies reserves. Supported GPU drivers/backends are assumed installed. Your own machine may differ — RAM, free disk space and whether an acceleration backend is actually installed all change the answer, which is what the PC scan measures directly.

GeForce RTX 5060 local AI — frequently asked questions

How many AI models can a GeForce RTX 5060 run?
Out of 225 models in the Ollama library, 158 have good/excellent reference fits on a GeForce RTX 5060's 8GB of VRAM at 4K context, and a further 44 run with part of the model offloaded to system RAM, more slowly.
What is the largest AI model a GeForce RTX 5060 can run?
The heaviest build that has a good/excellent reference fit is nous-hermes2:10.7b-solar-q4_K_M (10.7B parameters), needing about 7.6 GB of memory from a 6.0 GB download.
Is 8GB of VRAM enough for local AI?
8GB is enough for 158 of the 225 builds evaluated here, under the stated reference assumptions. Fit alone does not tell you whether a build is suitable for chat, coding or embeddings. Very large models still need either partial CPU offload or a card with more memory.

Compare other graphics cards

GeForce RTX 5090GeForce RTX 5080GeForce RTX 5070 TiGeForce RTX 5070GeForce RTX 5060 Ti 16GBGeForce RTX 4090GeForce RTX 4080GeForce RTX 4070 TiGeForce RTX 4070GeForce RTX 4060 Ti 16GBGeForce RTX 4060 Ti 8GBGeForce RTX 4060GeForce RTX 3090GeForce RTX 3080GeForce RTX 3070GeForce RTX 3060 12GBGeForce RTX 3060 TiGeForce RTX 3050 8GB