Current
RTX 4060 Ti 16GB
Good for 7B to 14B models, with selected 24B or MoE tests.
32 GB RAM16 GB VRAMchat
Local AI tool picker
Best Local AI Tool for RTX GPU: Ollama, LM Studio, Open WebUI, or Jan?
Choose the best local AI tool for an NVIDIA RTX GPU workstation, including Ollama, LM Studio, Open WebUI, Jan, CUDA workflows, VRAM limits, and local APIs.
Best first download
Qwen3 14B
Model rows
76
local model rows
Updated
Jun 28, 2026
metrics snapshot
Families
15
model families
Hardware simulator
Simulate a GPU upgrade before downloading a 20 GB model.
Compare the machine you have with the machine you might buy, then reverse-check the hardware needed for a target model.
Now fits
45
Target fits
59
Upgrade comparison
Target
RTX 4090
Good for strong 14B to 32B local coding and reasoning models.
64 GB RAM24 GB VRAMreasoning
Models unlocked by this upgrade
These did not fit or stretch on the current machine, but become realistic on the target.
Qwen3 30B-A3B
30B MoE / Q4 about 18 GB / Efficient MoE reasoning
Status
Fits comfortably
Score
95/100
Qwen3 32B
32B / Q4 about 20 GB / Workstation-grade open model
Status
Fits comfortably
Score
94/100
DeepSeek-R1 Distill Qwen 32B
32B / Q4 about 20 GB / Serious local reasoning
Status
Fits comfortably
Score
88/100
Qwen2.5-VL 32B
32B / Q4 about 22 GB / Large local multimodal analysis
Status
Fits comfortably
Score
82/100
GLM-4.7 Flash
30B-A3B MoE / Q4 about 18 GB / Efficient GLM deployment
Status
Fits comfortably
Score
78/100
Model requirement planner
Qwen3 14B
Useful when 8B is not consistent enough and you still want practical local speed.
RAM floor
24 GB
VRAM target
12 GB
Q4 size
9 GB
Install hint
ollama run qwen3:14bMinimum comfortable hardware paths
First exact: 32 GB RAM32 GB RAM
32 GB RAM / no dedicated GPU / usable model memory 17 GB
Fits comfortably
RTX 3060
32 GB RAM / 12 GB VRAM / usable model memory 12 GB
Fits comfortably
RTX 4070
32 GB RAM / 12 GB VRAM / usable model memory 12 GB
Fits comfortably
RTX 4070 Ti
32 GB RAM / 12 GB VRAM / usable model memory 12 GB
Fits comfortably
RTX 5070
32 GB RAM / 12 GB VRAM / usable model memory 12 GB
Fits comfortably
RTX 4060 Ti 16GB
32 GB RAM / 16 GB VRAM / usable model memory 16 GB
Fits comfortably
Qwen3 14B
AlibabaApache 2.0Higher-quality local reasoning
Useful when 8B is not consistent enough and you still want practical local speed.
Parameters
14B
Q4 size
9 GB
RAM floor
24 GB
VRAM target
12 GB
Performance
61/100
Pulls
31.5M
chatcodingreasoningWorkload match
Fit order
Performance + adoption + fit
#1
Match score
73/100
Adoption
94/100
Install hint
ollama run qwen3:14bTool answer
Best local AI tool for RTX GPU
For an RTX GPU workstation, install Ollama first if you care about repeatable GPU tests, local APIs, and automation. Add Open WebUI when the machine becomes a browser-based local AI station. Use LM Studio for desktop chat and manual model control, and treat Jan as an assistant workflow to compare after the GPU runtime is stable.
Updated with local model metrics
2026-06-28
Pick the model size with the simulator first, then choose the runtime or UI layer.
Decision matrix
Which local AI app should you install first?
Ollama
I want to test whether CUDA and VRAM are actually being used.
It keeps the first GPU test simple and makes it easier to compare model speed and memory behavior.
Open WebUI after Ollama
I want a browser UI for a local AI workstation.
A web UI is useful once the runtime, model path, and GPU behavior are stable.
LM Studio
I want desktop chat and model loading controls.
It is more comfortable for manual exploration and switching between local chat models.
Jan after baseline tests
I want a desktop assistant on the GPU machine.
Use it after you know the GPU machine can run the target model class comfortably.
Tool fit
Ollama, LM Studio, Jan, and Open WebUI are not the same decision.
Ollama
Best first install for GPU tests- Best for
- Repeatable RTX GPU tests, local APIs, coding assistants, scripts, and comparing model classes by VRAM behavior.
- Avoid when
- The user wants only a polished visual desktop chat and no command-line workflow.
- Install first on
- RTX 3060, RTX 4060 Ti 16GB, RTX 4090, RTX 5090, and Linux or Windows GPU desktops.
Open WebUI
Best second layer for a GPU box- Best for
- A browser interface on a shared RTX workstation, home server, or always-on local AI machine.
- Avoid when
- The model runtime is not stable yet or the user has not verified VRAM headroom.
- Install first on
- RTX workstations after the Ollama or compatible runtime path has been proven.
LM Studio
Best desktop chat option- Best for
- Manual model exploration, desktop chat, local server controls, and switching models without building a server UI.
- Avoid when
- The machine is intended to be an always-on shared service or benchmark runner.
- Install first on
- RTX desktops where one person wants a visual local chat and model browser.
Jan
Assistant workflow to compare- Best for
- A desktop assistant experience after the GPU baseline is known.
- Avoid when
- The first job is validating CUDA, VRAM, speed, or model fit.
- Install first on
- RTX desktops used by one person as a local assistant workstation.
Install order
Avoid turning tool setup into the hard part.
1
Use the GPU hardware guide first so you know whether the machine is a 7B, 14B, 32B, or larger-model workstation.
2
Install Ollama and run one model that should fit the VRAM budget.
3
Add LM Studio if you want a desktop chat workflow, or Open WebUI if the GPU box should become a browser-based local AI station.
4
Compare the same coding, reasoning, or chat prompt across tools before choosing a daily default.
Tool path by machine
RTX 3060 12GB
Ollama first, LM Studio for desktop chat
Use 7B to 14B-class models first and avoid making 32B the default story.
RTX 4060 Ti 16GB
Ollama for baseline tests, Open WebUI after stable 14B runs
This is a strong 7B to 14B tier and a cautious stretch tier for selected larger models.
RTX 4090 / 5090
Ollama plus Open WebUI for a local AI station
A 24GB or 32GB card makes the UI layer more valuable because the runtime can support stronger daily models.
Next pages
RTX 4060 Ti 16GB model guide
Use this for the common 16GB VRAM local AI workstation tier.
RTX 4090 model guide
Use this when 24GB VRAM and 32B-class models are the main question.
RTX 4060 Ti Qwen and DeepSeek scenario
Use this when the search already includes Qwen3 or DeepSeek.
Full local AI tool picker
Compare the same tools outside the RTX-specific context.
AI Jupyter scoring method
Check how the site treats VRAM fit, comfort, and real prompt testing.
Tool FAQ
What local AI tool should I install first on an RTX GPU?
Install Ollama first if you want to validate GPU behavior, VRAM fit, and repeatable local API tests. Add Open WebUI or LM Studio after the baseline model is stable.
Is Open WebUI better than LM Studio for an RTX workstation?
Open WebUI is better when the RTX machine is a shared or browser-based local AI station. LM Studio is better when one person wants desktop chat and model controls.
Should RTX 4090 users install Ollama or LM Studio?
Use Ollama for repeatable 24GB VRAM tests and local APIs. Use LM Studio when the priority is desktop chat and visual model exploration.
Does the tool decide whether a 32B model fits?
No. VRAM, RAM, quantization, context length, and workload decide fit first. The tool decides how comfortable the workflow is after the model can run.
More local AI tool scenarios