Running large language models (LLMs) on your personal hardware offers unmatched privacy, cost efficiency, and flexibility. NVIDIA's upcoming RTX 50 series GPUs are poised to redefine what's possible, bringing unprecedented VRAM and computational power to local inference setups.

Choosing the right LLM for your specific RTX 50 series card requires a careful understanding of GPU specifications, model characteristics, and available software. Success hinges on effectively matching a model's VRAM and compute demands with your hardware's capabilities.

The Guide to RTX 50 Series GPUs for LLMs

The primary factor determining a GPU's suitability for LLMs is its Video Random Access Memory (VRAM). Larger models, or those employing less aggressive compression (quantization), demand more VRAM. Other important aspects like CUDA core count, Tensor Core performance (especially for lower precision formats like FP16/INT8), and memory bandwidth directly influence inference speed, measured in tokens per second.

Here's a detailed look at the VRAM allocations across the RTX 50 series desktop lineup, along with their capabilities for LLM workloads:

GPU Model	VRAM (GB)	Typical LLM Use Case Potential
RTX 5090	32	Bleeding Edge: Very large models (100B+ Q*), multi-model inference, high-performance fine-tuning.
RTX 5080 / Super	16 / 24*	High-End: Large models (70B+ Q*), advanced fine-tuning, and high throughput. (24GB for Super variant rumored)
RTX 5070 Ti Super*	16 / 18*	Upper-Mid Range: Large models (70B+ Q*), excellent performance for its tier. (18GB rumored for Super variant)
RTX 5070 Ti	16	Mid-High Range: Medium-Large models (13B-70B Q*), robust performance.
RTX 5070	12	Mid-Range: Medium models (7B-30B Q*), strong performance for most common LLMs.
RTX 5060 Ti	8 / 12*	Mid-Range / Value: Small-Medium models (7B-13B Q*), good value for performance. (12GB for some variants rumored)
RTX 5060	8	Entry-Mid Level: Small models (3B-7B Q*), suitable for basic LLM interaction.
RTX 5050	8	Entry-Level: Small models (3B-7B Q), basic LLM use, potentially limited by raw processing power for more complex tasks.

* Q denotes quantized models. Performance varies significantly within tiers based on specific model, quantization, and framework. Note that for some larger models, even with quantization, partial offloading to CPU RAM might be necessary, impacting performance.

For optimal performance and compatibility, always ensure you have the latest NVIDIA drivers installed. These updates frequently include performance enhancements and critical fixes for CUDA and LLM operations.

Essential Factors for Choosing a Local LLM

Selecting an LLM involves balancing several important technical considerations:

Model Size (Parameters)

LLMs are categorized by their number of parameters (e.g., 7 billion, 13B, 70B, 100B+). Generally, larger models offer better reasoning and broader knowledge but demand significantly more VRAM and compute power. VRAM requirements scale roughly linearly with parameter count and depend heavily on the precision used (e.g., FP16, INT8, INT4).

Quantization Techniques

Quantization is a vital technique for reducing the memory footprint and often accelerating the inference speed of LLMs. It achieves this by representing the model's weights and activations with lower-precision data types (e.g., 8-bit or 4-bit integers) instead of standard 16-bit floating-point. Notable quantization formats include:

GGUF (GPT-Generated Unified Format): Widely used by llama.cpp, this format bundles the model and quantization information, supporting various methods (e.g., Q4_K_M, Q5_K_S, Q8_0) optimized for different quality/performance trade-offs.
GPTQ (Generative Pre-trained Transformer Quantization): An earlier, popular post-training method often requiring specific library support.
AWQ (Activation-aware Weight Quantization): This method aims to preserve model quality more effectively during quantization.
BitsAndBytes: A library integrated with Hugging Face Transformers that enables on-the-fly quantization (e.g., loading models in 8-bit or 4-bit directly).
Quantization-Aware Training (QAT): A growing trend where models are trained with quantization in mind, leading to superior quality retention at very low bitrates (e.g., Google's Gemma QAT models).

Quantization dramatically lowers VRAM usage, enabling the execution of larger models on GPUs with more limited memory. For instance, a 7B parameter model might need around 14GB in FP16 but only about 4-5GB using 4-bit quantization (like Q4_K_M).

Inference Speed

Inference speed, measured in tokens per second (tok/s), indicates how quickly the model generates text. It is influenced by the GPU's compute power (CUDA/Tensor cores), memory bandwidth, model size, quantization level, batch size, and the efficiency of the inference framework.

Task Suitability

Models are frequently fine-tuned for specific tasks. Some are general-purpose chat models (e.g., Llama 3.1 Instruct), while others specialize in coding (e.g., Code Llama), instruction following, or particular knowledge domains. Select a model whose training aligns with your intended use.

Licensing Agreements

LLMs come with various licenses that dictate their usage, modification, and distribution. Common licenses include Apache 2.0 (permissive), MIT, and specific community licenses like the Llama 2 & 3 Community License, which may impose restrictions on commercial use for larger entities. Always review the model's license before deployment, especially for commercial applications.

The relationship between GPU VRAM, Model Size, Quantization, and Local LLM Execution.

Top LLMs for Your RTX 50 Series GPU

These recommendations highlight popular, high-performing models and assume the use of quantization (primarily 4-bit or 5-bit GGUF variants like Q4_K_M or Q5_K_M) unless explicitly stated. Performance is qualitative and can vary based on specific system configurations.

RTX 5090 (32GB VRAM)

As the flagship consumer GPU, the RTX 5090's 32GB VRAM enables it to handle the largest available models with minimal quantization or smaller models at higher precision for peak performance.

Llama 3.1 70B Instruct (Quantized): A top-tier open model, runs very well with 4-bit or 5-bit quantization (e.g., Q4_K_M, Q5_K_M GGUF). The 3.1 version offers improved context and capabilities.
Command R+ (Quantized): Cohere's formidable 104B parameter model. With 32GB, you can load a 4-bit quantized version (e.g., Q4_K_M, requiring around 55GB of VRAM in FP16) and expect better performance than on a 24GB card.
Gemma 3 27B QAT (Quantized): Google's highly optimized Quantization-Aware Trained model. In INT4, it requires only around 14.1GB VRAM, leaving ample room for larger context windows or other applications.
Mixtral 8x7B (Quantized): This high-quality Mixture-of-Experts (MoE) model, while having 47B parameters, only activates 13B per token. A 4-bit quantized version (Q4_K_M) typically needs 25-30GB VRAM, making it a comfortable fit for the 5090 without CPU offloading.
Mixtral 8x22B (Heavily Quantized/Offloaded): This larger MoE model (141B parameters) is very VRAM intensive (70GB+ for Q4). While it's generally aimed at multi-GPU setups, the 5090 might handle extremely aggressive quantization (e.g., Q3_K) with significant CPU offloading, though with considerable performance trade-offs.
DeepSeek-R1-Distill-Qwen-32B (Quantized): Runs very comfortably at 4-bit quantization (~18GB VRAM), offering excellent performance for its size.
Fine-tuned 70B+ Models: The 5090 is ideal for running specialized, fine-tuned models based on cutting-edge architectures at the highest quality settings.

RTX 5080 / Super & RTX 5070 Ti Super (16GB / 18GB / 24GB VRAM)

These GPUs offer a powerful balance, capable of running large models with effective quantization and providing excellent throughput.

Llama 3.1 70B Instruct (Heavily Quantized/Offloaded): Possible with aggressive 4-bit (e.g., Q4_0 or Q4_K_S) or 5-bit (Q5_0) quantization, but will likely require some layers offloaded to CPU RAM, impacting speed. For the 24GB 5080 Super, a 4-bit Q4_K_M might fit entirely.
Llama 3.1 8B Instruct (Unquantized/Quantized): Runs exceptionally well, even unquantized (FP16, around 16GB VRAM, a perfect fit for 16GB models). Quantized versions (Q4_K_M, ~5GB VRAM) deliver blazing-fast inference.
Gemma 3 12B QAT (Quantized): Highly recommended for these cards. The INT4 version requires only about 6.6GB VRAM, leaving plenty of headroom.
Mixtral 8x7B (Quantized): Fits well with 4-bit quantization (e.g., Q4_K_M, ~25-30GB VRAM needed, so the 24GB 5080 Super will be able to handle it if the models are efficient). For 16GB cards, it will necessitate significant CPU offloading or very aggressive quantization, impacting performance.
Mistral 7B / Zephyr / OpenHermes (Unquantized/Quantized): Smaller, highly efficient models that run extremely fast, making them ideal for high-throughput applications.
Phi-3 Medium (Quantized): Microsoft's capable small model runs very well.
DeepSeek-R1-Distill-Qwen-14B (Quantized): Runs comfortably with 4-bit quantization (~8GB VRAM), offering a great blend of performance and size.
Fine-tuned 13B-30B Models (Quantized): Excellent for running specialized 13B to 30B models comfortably.

RTX 5070 Ti (16GB VRAM)

This 16GB card is a strong performer in the mid-high range, suitable for medium-large models with quantization.

Llama 3.1 8B Instruct (Unquantized/Quantized): Runs exceptionally well. FP16 (~16GB) is a perfect fit, and high-quality quantization (Q5_K_M, Q8_0) fits easily with superior speed.
Gemma 3 12B QAT (Quantized): A great option. The INT4 version fits comfortably at around 6.6GB VRAM.
Mistral 7B Instruct (Unquantized/Quantized): Similar to Llama 3.1 8B, it runs extremely well and easily fits even with less aggressive quantization.
Phi-3 Medium (Quantized): Runs comfortably with 4-bit quantization (~7GB VRAM needed).
DeepSeek-R1-Distill-Qwen-14B (Quantized): Also runs well, especially with Q4 quantization (~8GB VRAM).
Mixtral 8x7B (Heavily Quantized/Offloaded): Possible with aggressive 3-bit or lower 4-bit quantization (e.g., Q3_K_M, Q4_0), but will likely require some CPU offloading and performance will be noticeably slower.
Fine-tuned 13B Models: An excellent platform for running specialized 13B models.

RTX 5070 (12GB VRAM)

This 12GB card is a solid mid-range option, well-suited for medium-sized models with quantization.

Llama 3.1 8B Instruct (Quantized): Runs very well. While FP16 (~16GB) won't fit entirely, high-quality quantization (Q5_K_M, Q8_0) fits easily within 12GB with great speed.
Gemma 2 7B / Gemma 3 7B QAT (Quantized): Excellent choices that balance quality and VRAM usage for these cards. The Gemma 3 7B QAT in INT4 is highly efficient.
Mistral 7B Instruct (Unquantized/Quantized): Similar to Llama 3.1 8B, runs extremely well and easily fits even with less aggressive quantization.
Phi-3 Medium (Quantized): Runs comfortably with 4-bit quantization (~7GB VRAM needed).
DeepSeek-R1-Distill-Qwen-7B (Quantized): Also runs well, especially with Q4 quantization.
Mixtral 8x7B (Very Heavily Quantized/Offloaded): Will be highly challenged to run on 12GB. Only possible with very aggressive 3-bit quantization and significant CPU offloading, leading to slow performance and potential quality degradation.
Fine-tuned 7B/8B Models: Excellent platform for running specialized 7B/8B models.

RTX 5060 Ti (8GB / 12GB VRAM)

The 5060 Ti variants offer versatility. The 12GB version bridges the gap to the 5070, while the 8GB version remains an entry point.

For 12GB VRAM variant: Can handle models similar to the RTX 5070 (e.g., Llama 3.1 8B quantized, Gemma 3 7B QAT, Phi-3 Medium). Performance will be slightly lower due to potentially fewer CUDA cores.
For 8GB VRAM variant:
- Llama 3.1 8B Instruct (Quantized): Runs well with 4-bit quantization (Q4_K_M fits comfortably within 8GB).
- Mistral 7B Instruct (Quantized): Similar to Llama 3.1 8B, runs well with 4-bit quantization.
- Phi-3 Mini / Small (Quantized): These smaller models (3.8B for Mini, 7B for Small) are ideal for 8GB cards and offer good performance and quality for their size.
- Gemma 2B QAT / Gemma 7B QAT (Quantized): Very efficient and performant for their size, fitting well within 8GB.
- DeepSeek-R1-Distill-Qwen-1.5B (Quantized): Light and fast, great on 8GB cards.
- Other < 7B Models: Models like StableLM 3B and TinyLlama 1.1B run very fast.

RTX 5060 (8GB VRAM) & RTX 5050 (8GB VRAM)

These entry-level cards are most constrained by VRAM. Focus on smaller models or heavily quantized versions of medium models.

Llama 3.1 8B Instruct (Quantized): Runs well with 4-bit quantization (Q4_K_M fits comfortably within 8GB).
Mistral 7B Instruct (Quantized): Similar to Llama 3.1 8B, runs well with 4-bit quantization.
Phi-3 Mini / Small (Quantized): Ideal for 8GB cards, offering impressive capabilities for their size.
Gemma 2B QAT / Gemma 7B QAT (Quantized): Very efficient and performant for their size.
DeepSeek-R1-Distill-Qwen-1.5B (Quantized): Light and fast, excellent on 8GB cards.
Other < 7B Models: Models like StableLM 3B and TinyLlama 1.1B run very fast.
Larger Models (e.g., 13B): Possible only with very aggressive quantization (e.g., Q2_K, Q3_K variants), expect slower performance and potential quality degradation. The 5050 might also be limited by raw processing power for more demanding 7B models.

How to Optimize Your Local LLM Setup: Tools and Frameworks

Several powerful tools simplify the process of downloading and running LLMs locally, ensuring you maximize your RTX 50 series GPU's potential:

Ollama: Offers a remarkably simple command-line interface and local server. It provides easy setup and model management for various GGUF models.
```
# Pull and run Llama 3.1 8B
ollama run llama3.1:8b

# List downloaded models
ollama list
```
LM Studio / Jan: User-friendly graphical interfaces (GUIs) that streamline the download and interaction with a wide array of LLMs, often leveraging llama.cpp in the backend for efficient execution.
Text Generation WebUI (Oobabooga): A comprehensive Gradio-based web interface that supports diverse models, loaders (Transformers, ExLlamaV2, Llama.cpp), and advanced features like fine-tuning and various chat modes.
Llama.cpp: A high-performance inference engine written in C++. It primarily utilizes the GGUF format and supports both CPU and GPU (via CUDA/Metal) acceleration. While it requires compilation, it offers fine-grained control over model execution.
```
# Example: Run Llama 3.1 8B GGUF with GPU offload
./main -m ./models/llama-3.1-8b-instruct.Q4_K_M.gguf \
       -p "User: Explain the concept of attention in Transformers.\nAssistant:" \
       -n 512 --color -ngl 33 # -ngl: num GPU layers
```

Hugging Face Transformers: The industry-standard Python library for natural language processing. It can load and run many models, often integrated with accelerate for multi-GPU/CPU offloading and bitsandbytes for dynamic quantization.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load with 4-bit quantization for VRAM efficiency
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map= "auto", # Automatically distribute layers
    load_in_4bit=True
)

# Basic generation example
inputs = tokenizer(
    "Generate a short story about a detective in a futuristic city:",
    return_tensors="pt"
).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vLLM / TensorRT-LLM: Specialized inference libraries designed for maximizing throughput and minimizing latency, particularly for serving scenarios. These typically involve model conversion steps and are best suited for advanced users prioritizing raw speed.

Tips for Benchmarking and Performance Tuning

While published benchmarks offer a starting point, the most effective way to assess performance is to test models directly on your hardware and with your specific workload.

Benchmarking: Many tools include built-in benchmarks (e.g., llama.cpp features perplexity calculation and speed tests). Simple Python scripts using the time module can accurately measure generation speed for particular prompts.
Primary Metrics: Focus on tokens per second (generation speed) and time-to-first-token (latency), which are crucial for real-time applications.
Tuning Strategies:
- Quantization Level: Experiment with different GGUF quantizations (e.g., Q4_K_M vs. Q5_K_M vs. Q8_0) or bitsandbytes settings. This helps balance VRAM consumption, inference speed, and output quality.
- GPU Layer Offloading (-ngl in Llama.cpp): For GGUF models, adjust the number of layers offloaded to the GPU. Maximize this value within your VRAM limits for the best speed. Start high and decrease if you encounter out-of-memory errors.
- Batch Size: In frameworks supporting batching (like Transformers, vLLM), increasing the batch size can significantly improve overall throughput, often with efficient VRAM usage.
- Context Length: Longer context windows consume more VRAM due to the growing KV cache. Adjust this setting based on your application's needs and available memory.
- Framework Choice: Different frameworks exhibit varying performance characteristics. Llama.cpp, ExLlamaV2, and TensorRT-LLM are frequently among the fastest for inference workloads.
- Software Stack: Ensure your CUDA Toolkit, cuDNN, PyTorch, and framework versions are compatible and up-to-date. Regular updates can bring notable performance gains.

Conclusion

The NVIDIA RTX 50 series GPUs delivers an unparalleled platform for running a wide spectrum of LLMs locally. VRAM capacity remains the most important factor determining model feasibility, with the 32GB RTX 5090 offering the most flexibility for cutting-edge applications, while the 8GB RTX 5060/5050 provides an accessible entry point for smaller or heavily quantized models.

Advanced quantization techniques, including GGUF and the emerging QAT-optimized models, are essential for fitting larger models into available VRAM and achieving impressive performance. Choosing the right LLM involves a strategic balance of model size, task suitability, and licensing, all weighed against the specific VRAM and performance characteristics of your RTX 50 GPU.

Best Local LLMs for Every NVIDIA RTX 50 Series GPU