GeForce RTX 50 Series Graphics Cards AI for Creation

Shape Your AI with MSI

Q: What do INT4, INT8 and FP16 mean, and which should I use?

Quantization formats: the bytes used per model parameter. FP16 is about 2 bytes, INT8 about 1, and INT4 about 0.5-0.6 (for example Q4_K_M in the GGUF format). Lower precision fits a larger model into less VRAM, at some cost in quality. A 13B-class model needs about 26GB at FP16, 13GB at INT8 and 7-8GB at INT4. Use INT8 when it fits, INT4 when it does not.

Q: Is NVIDIA or Radeon better for running AI workloads?

It depends mainly on your software. Most local AI tooling, including llama.cpp, Ollama, LM Studio, vLLM and PyTorch, ships CUDA as its default path, so NVIDIA GPUs usually run models with no extra setup, while Radeon depends on ROCm, which covers fewer frameworks and operating systems. VRAM also sets the ceiling on model size, and the GeForce RTX 5090's 32GB is the most of any GeForce card. For most local AI builds today, GeForce RTX tends to be the more straightforward starting point.

Q: How do Tensor Cores improve AI performance on GeForce RTX GPUs?

Tensor Cores are dedicated matrix multiply-accumulate units, the operation AI workloads spend most of their time on, and one that general-purpose CUDA cores handle far less efficiently. On GeForce RTX 50 Series GPUs, 5th generation Tensor Cores add FP4 alongside FP8 and FP16, delivering from 988 AI TOPS on the RTX 5070 to 3,352 AI TOPS on the RTX 5090.

AI for Creation
AI for On-Device Intelligence
AI for Gaming

Build an AI Powerhouse with MSI GeForce RTX 50 Series

Experience the future of intelligent computing with MSI's GeForce RTX 50 Series GPUs, featuring NVIDIA's revolutionary Blackwell Architecture. They enable powerful local AI deployments to deliver unmatched precision, speed, and data privacy.

Guru3D

“The card’s overall construction reflects MSI’s focus on reliability rather than aesthetics alone, resulting in a product that is engineered for continuous operation under demanding conditions”

Guru3D

“As a piece of hardware and design, the Vanguard is a well executed product, it runs cool, is silent and looks nice, I'd prefer it even over the founder edition card”

TechPowerUp

"MSI is up to the challenge with its SUPRIM line of graphics cards that don't just look like pieces of jewellery, but are also designed to be the fastest custom design graphics cards out of the box."

TechPowerUp

“MSI's card on the other hand runs whisper-quiet out of the box! With just 28 dBA under full load it is the quietest RTX 5090 that we've tested today—very impressive work!”

PCMag Readers’ Choice 2025

"MSI proves that it’s the graphics card brand that gamers—or anyone who wants top-notch PC visuals—should buy. It rates the best for satisfaction, cost, reliability, and graphics performance."

Hardware Premium

“This gaming beast not only represents MSI's most premium range. It also raises the base specifications of NVIDIA to a new level with its impressive Hyper Frozr cooling system”

AI for Creation

AI-Enhanced Video & Image Editing

Eliminate repetitive tasks and supercharge your workflow with AI acceleration in over 500 apps to transform your workflow with AI upscaling, denoising, and generative fills. Preview edits in real-time, even with high-res footage, so you spend more time creating and less time waiting.

Harness Generative AI

Unleash the power of the most advanced Generative AI models to automate and supercharge tasks like scene composition, lighting, and even asset generation – allowing you to iterate, experiment, and realize your creative vision faster than ever.

AI-Accelerated 3D Rendering

Reduce long render queues and VRAM bottlenecks with advanced AI denoising, smart memory management, and neural rendering. MSI's GeForce RTX 50 Series Graphics Cards unlock a new tier of performance in professional CG workloads with complex scenes.

AI for On-Device Intelligence

Large Language Model Training and Inference

LLM Training and Inference

Harness the power of NVIDIA's Blackwell Architecture, optimized specifically for AI performance with advanced tensor cores for ML workloads, larger VRAM buffer, and more. With significantly improved AI TOPS, MSI GeForce RTX 50 Series GPUs deliver industry-leading performance, enabling seamless training and running larger LLMs and multimodal models with ease.

Chat, Create, and Command with Generative AI

Limitless Creation with Gen AI

Run advanced AI models like Stable Diffusion, DeepSeek, Llama, and more, on your own hardware to access the full power of Generative AI at your desk. MSI's GeForce RTX 50 Series come equipped with the AI processing power and VRAM you need to host cutting-edge chatbots, generate stunning images and videos, and do so much more in the blink of an eye.

Unmatched Data Privacy and Security

Keep your sensitive data, documents, and results secure by running AI workloads entirely on your own hardware. Use self-hosted AI models to ensure that your data never leaves your system – giving you full control, security, and privacy for confidential projects.

AI for Gaming

DLSS 4 Multi-Frame Generation

Enjoy ultra-smooth gameplay at high resolutions without compromising on graphics or turning down ray tracing. DLSS 4 uses 5th Generation Tensor cores in the GeForce RTX 50 Series to generate more frames to minimize stutter and lag and deliver a fluid gaming experience – so you can stay focused on the action.

AI-Enhanced Ray Tracing

Experience unmatched realism and fluidity in next-gen games with Ray Tracing, thanks to Ray Reconstruction. Powered by a brand-new AI model that recreates higher quality ray-traced images, it is designed to unlock much higher frame rates without sacrificing quality.

AI-Enhanced Livestreaming

Elevate your livestreams with AI-driven features within NVIDIA Broadcast, like Studio Voice, Virtual Key Light, Virtual Backgrounds, and so much more on GeForce RTX 50 Series Graphics Cards – instantly transforming your space into a professional studio.

Recommended Hardware Requirements for Local AI Deployments

Recommended hardware requirements for local AI deployments on MSI GeForce RTX 50 Series graphics cards
GPU	GeForce RTX 5090	GeForce RTX 5080	GeForce RTX 5070 Ti	GeForce RTX 5070
VRAM	32GB GDDR7	16GB GDDR7	16GB GDDR7	12GB GDDR7
Memory Bandwidth	1,792 GB/sec	960 GB/sec	896 GB/sec	672 GB/sec
AI TOPS	3,352 AI TOPS	1,801 AI TOPS	1,406 AI TOPS	988 AI TOPS
Local LLM — largest model that fits fully on-GPU	30B-class at INT4 (~15–18GB) 13B-class at FP16 (~26GB)	13B-class at INT8 (~13GB) 30B-class at INT4 (~15–18GB) exceeds 16GB once headroom is added	13B-class at INT8 (~13GB) 30B-class at INT4 (~15–18GB) exceeds 16GB once headroom is added	7B–8B-class at INT8 (~8GB) 13B-class at INT4 (~7–8GB)
Recommended Use-Case	Serious local AI — run models uncompressed, handle very long documents. Only RTX 50 desktop GPU with 32GB.	Professional local AI — faster on long prompts and multi-step AI agents.	Everyday local AI — chat, coding and summaries, plus 1440p gaming.	Your first local AI PC — 7B–8B-class open models.
Gaming	4K DLSS + Full RT	4K DLSS + RT	1440p DLSS + RT	1440p-1080p DLSS + RT
Content and Production	Complex 3D Modeling / 4K/8K Video Editing / Complex 3D Rendering	3D Modeling / 4K Video Editing / 3D Rendering / Broadcasting and Streaming	Pro Streaming / Heavy Video Editing / Rendering	Video Editing / Casual Streaming / Light Rendering

VRAM, Memory Bandwidth and AI TOPS figures are NVIDIA published specifications for the GeForce RTX 50 Series. Source: https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/, retrieved 2026-08-04.

Model VRAM figures are weight-only estimates (parameters × bytes per parameter: 2 bytes at FP16, 1 byte at INT8, approximately 0.5–0.6 bytes at INT4 depending on the quantization method, for example Q4_K_M). All figures are for inference only, based on an 8K context window; longer context windows require more, and training or fine-tuning requires substantially more VRAM. Budget approximately 20% above the weight-only figure for KV cache and runtime overhead.

“Fits fully on-GPU” means model weights load entirely into GPU VRAM for optimal speed; larger models can still run with CPU offload at reduced throughput.

VRAM Requirements for LLMs

The GeForce RTX 50 Series gives you access to a range of GPUs for diverse local AI deployments across home offices, organizations, educational institutions, and more!

VRAM requirements for running local LLMs, with matching MSI GeForce RTX graphics cards
Use Case	VRAM Needed	Recommended Models (weight size)	MSI GeForce RTX GPU
Educational Projects and Personal Use	up to 8GB	7B–8B-class at INT4 (~4–5GB)	GeForce RTX™ 5060 8G GAMING TRIO OC GeForce RTX™ 5050 8G GAMING OC
Enthusiast and Light Development	12GB	8B-class at INT8 (~8GB) 13B-class at INT4 (~7–8GB)	GeForce RTX™ 5070 12G VANGUARD SOC
Professional Use and Individual Developers	16GB	13B-class at INT8 (~13GB)	GeForce RTX™ 5080 16G SUPRIM SOC GeForce RTX™ 5070 Ti 16G VANGUARD SOC
Studios and Creative Outlets	32GB	30B-class at INT4 (~15–18GB) 13B-class at FP16 (~26GB)	GeForce RTX™ 5090 32G SUPRIM SOC
Small Research Teams	64GB (2× 32GB, multi-GPU)	70B-class at INT4 (~35–42GB)	2× GeForce RTX™ 5090 32G SUPRIM SOC
Enterprise & Research Labs	≥ 84GB	70B-class at INT8 or larger models (~70GB and up)	Multi-GPU workstation or data-center class

Figures are weight-only estimates (parameters × bytes per parameter: 2 bytes at FP16, 1 byte at INT8, approximately 0.5–0.6 bytes at INT4 depending on the quantization method, for example Q4_K_M). All figures are for inference only, based on an 8K context window; longer context windows require more, and training or fine-tuning requires substantially more VRAM. Budget approximately 20% above the weight-only figure for KV cache and runtime overhead.

Recommended models load entirely into GPU VRAM at the level shown; larger models can still run with CPU offload at reduced throughput.

Multi-GPU configurations pool VRAM over PCIe — GeForce RTX 50 Series does not support NVLink — and require a framework that splits layers across devices.

The MSI Advantage: Engineered for Robust Local AI

Exploded view of an MSI GeForce RTX 50 Series graphics card showing the tri-fan shroud, heatsink fin stack, GPU die and backplate

Sustained Peak AI Performance

MSI's innovative thermal solutions — including the latest Hyper Frozr thermal design with cutting-edge STORMFORCE Fans, an Advanced Vapor Chamber, and a plethora of heatsink innovations — unlock peak performance even when running complex local AI workloads. They also enable unmatched stability when pushing your hardware to the limit with these demanding tasks.

MSI GeForce RTX 50 Series graphics cards from five different sub-series shown together

Cutting-Edge GeForce RTX 50 Series GPUs

Leverage NVIDIA's latest Blackwell Architecture with its 5th Generation Tensor Cores in MSI's GeForce RTX 50 Series Graphics Cards, unlocking superior AI inferencing and training performance. They are designed to deliver an incredible experience for efficient local and edge deployments of the most complex AI models.

MSI GeForce RTX 50 Series VENTUS graphics card in white, shown beside a matching MSI PC chassis

Tailored Solutions for Every Scenario

Whether you're building a compact workstation with a Mini-ITX motherboard or a large AI powerhouse with plenty of extensibility, MSI's GeForce RTX 50 Series lineup features a broad range of options for your every need.

MSI AI PC build with a GeForce RTX 50 Series graphics card installed in a glass-panel MSI chassis

Future-Proof and Upgradable

Performance

Oh Chip—That's Fast

The AI processors in every GeForce RTX GPU deliver chart-busting levels of performance across the most demanding games, apps, and workflows.

Content Creation

Immersive Gaming

Accelerated Development

Enhanced Productivity

GeForce RTX 5090

Apple Mac Studio M2 Ultra

3D Design

Video Editing

3.8X

Generative AI

2.6X

50 min

100 min

150 min

200 min

250 min

Shorter wait times are better.

Performance testing conducted by NVIDIA in December 2024 with desktops equipped with Intel Core i9-14900K and 64GB RAM. NVIDIA Driver 571.24, Windows 11. Time scaled to 10 minutes for comparison purposes. Maya with Arnold 2025 (7.3.0) renderer performance measures render time of the NVIDIA SOL 3D model. DaVinci Resolve PugetBench’s GPU Score measures various GPU-accelerated affects, including Magic Mask, Depth Map, Speed Warp, and others. Flux.dev measures the time to generate an image with FP4 on GeForce RTX 50 Series, FP16 on 40 Series. M2 Ultra is measured running Flux.dev on Draw Things. 30 steps, 1024x1024 resolution.

DLSS On

DLSS Off

Cyberpunk 2077 (RT: Overdrive Tech Preview)

Marvel's Spider-Man: Miles Morales

2.1X

Portal With RTX

5.6X

Warhammer 40,000: Darktide

Relative Performance (FPS)

Cross-generation reference — RTX 40 Series data. Measured on GeForce RTX 4090 (previous generation), i9-12900K, 32GB RAM, Win 11 x64, 3840×2160 resolution, highest game settings, DLSS Super Resolution Performance Mode with DLSS Frame Generation. GeForce RTX 50 Series supports DLSS 4.5 with 6X Multi Frame Generation, which is not represented in this chart.

GeForce RTX 4090 (previous generation)

Apple M2 Ultra

Model Training and Fine-Tuning

359.6

Code Assistant

106.8

100

200

300

400

Relative Performance (tokens per second)

Cross-generation reference — RTX 40 Series data. Relative Performance (tokens per second): Model Training/Fine Tuning of BERT-Base-Cased on GeForce RTX 4090 (previous generation) using mixed precision. GeForce RTX 50 Series figures are not represented in this chart. Code assist is Code llama 13B Int4 inference performance INSEQ=100, OUTSEQ=100 batch size 1

With TensorRT-LLM

Without TensorRT-LLM

Batch size = 8

829

216

Batch size = 4

677

166

Batch size = 1

188

137

200

400

600

800

1000

Relative Performance (tokens per second)

Powerful, Secure Local and Advanced AI Deployments
with the MSI AI Ecosystem

FAQ Section

Sizing your hardware

Q: What are the most important hardware factors for running LLMs locally?

A: Your GPU's VRAM and AI computing capabilities are the most important factors for local AI performance, followed by system RAM and a modern CPU. Sufficient VRAM determines the maximum model size you can run efficiently on your PC.

Q: How much VRAM is required for different sizes of AI models?

A: Weights take about 2 bytes per parameter at FP16, 1 byte at INT8 and 0.5–0.6 bytes at INT4. So a 7B–8B model needs about 8GB at INT8; a 13B about 13GB at INT8 or 7–8GB at INT4; a 30B about 15–18GB at INT4 or 30GB at INT8; a 70B about 35–42GB at INT4 — more than one 32GB card holds. Add about 20% for KV cache and runtime overhead. These are inference figures; training needs substantially more.

Q: What do INT4, INT8 and FP16 mean, and which should I use?

A: Quantization formats — the bytes used per model parameter. FP16 is about 2 bytes, INT8 about 1, and INT4 about 0.5–0.6 (for example Q4_K_M in the GGUF format). Lower precision fits a larger model into less VRAM, at some cost in quality. A 13B-class model needs about 26GB at FP16, 13GB at INT8 and 7–8GB at INT4. Use INT8 when it fits, INT4 when it does not.

Q: What are the minimum PC specifications needed for running LLMs locally?

A: 8GB of VRAM, 32GB of system RAM and a modern 6-core CPU. VRAM sets the ceiling on model size: 8GB runs 7B–8B-class models at INT4, 12GB runs them at INT8, 16GB runs 13B-class at INT8, and 32GB runs 30B-class at INT4. System RAM only matters once a model exceeds VRAM and layers offload to the CPU, which cuts throughput.

Choosing a GPU

Q: Which GPUs are best for running complex local AI models (like LLaMA, GPT, Mistral, etc.) and handling advanced AI workloads?

A: The GeForce RTX 5090 is the strongest option in the RTX 50 Series for local AI: its 32GB of VRAM runs 30B-class models at INT4, or 13B-class models at FP16, entirely on-GPU. The 16GB RTX 5080 and RTX 5070 Ti run 13B-class models at INT8; the 12GB RTX 5070 runs 7B–8B-class models at INT8. VRAM sets the ceiling on model size.

Q: Is it better to use multiple lower-end GPUs or a single high-end GPU?

A: Use one card if the model fits. Multi-GPU only matters when it does not: two 32GB GeForce RTX 5090 cards pool 64GB, enough for a 70B-class model at INT4 (about 35–42GB of weights). Two limits — GeForce RTX 50 Series has no NVLink, so cards communicate over PCIe, and the framework must split model layers across devices (llama.cpp, vLLM and Hugging Face Accelerate do; most applications do not).

Q: Is NVIDIA or Radeon better for running AI workloads?

A: It depends mainly on your software. Most local AI tooling — llama.cpp, Ollama, LM Studio, vLLM and PyTorch — ships CUDA as its default path, so NVIDIA GPUs usually run models with no extra setup, while Radeon depends on ROCm, which covers fewer frameworks and operating systems. VRAM also sets the ceiling on model size, and the GeForce RTX 5090's 32GB is the most of any GeForce card. For most local AI builds today, GeForce RTX tends to be the more straightforward starting point.

Q: How do GeForce RTX GPUs compare to Apple's M-series chips for local AI processing tasks?

A: Different trade-offs. Apple's unified memory lets a high-configuration Mac allocate more memory to a model than any single consumer GPU. GeForce RTX leads on bandwidth and software: the RTX 5090 delivers 1,792 GB/sec, which governs token generation speed, and CUDA is the default target for nearly every AI framework. Choose Apple for the largest models at low power; choose GeForce RTX for the fastest tokens per second.

Software and setup

Q: What software do I need to run a local LLM on a GeForce RTX GPU?

A: Ollama (simplest, command line), LM Studio (graphical, with a built-in model browser) or llama.cpp (most control) — all free, all run GGUF models, and all use CUDA on GeForce RTX GPUs with no extra configuration. Use vLLM to serve a model to multiple users or applications. You do not need to install CUDA separately; the display driver is enough.

How it works

Q: How do Tensor Cores improve AI performance on GeForce RTX GPUs?

A: Tensor Cores are dedicated matrix multiply-accumulate units — the operation AI workloads spend most of their time on, and one that general-purpose CUDA cores handle far less efficiently. On GeForce RTX 50 Series GPUs, 5th generation Tensor Cores add FP4 alongside FP8 and FP16, delivering from 988 AI TOPS on the RTX 5070 to 3,352 AI TOPS on the RTX 5090.

Q: How important is the CPU compared to GPU for AI performance?

A: The GPU is far more relevant for inference speed and AI processing in games, creative apps, etc. The CPU only comes into play if you're running models larger than your VRAM or using CPU-specific features in some apps.

Q: How stressful is running AI workloads or hosting local AI on my hardware and will it damage components?

A: Running LLMs is similar in stress to gaming or rendering and won't damage hardware if cooling is adequate. Occasional heavy loads are perfectly safe for modern components as long as the cooling solution can handle heat effectively.

Q: What performance boost does DLSS 4.5 provide on GeForce RTX 50 Series GPUs?

A: DLSS 4.5 adds two GeForce RTX 50 Series exclusives: 6X Multi Frame Generation, which generates up to five additional frames per traditionally rendered frame, and Dynamic Multi Frame Generation, which adjusts the multiplier to the workload. Its 2nd generation transformer model for Super Resolution runs on all GeForce RTX GPUs. Actual frame rates depend on the GPU, game and resolution.

Shape Your AI with MSI

Build an AI Powerhouse with MSI GeForce RTX 50 Series