NVIDIA GeForce RTX 5090 Founders Edition

• FP16 Performance: 209.6 TFLOPS
• INT8 Performance: 3352 TOPS (via 5th Gen Tensor Cores)
• CUDA Cores: 21,760
• Tensor Cores: 680

The inclusion of 5th-generation Tensor Cores specifically accelerates FP8 and INT8 precision formats, which are increasingly the standard for optimized local inference. With 3352 TOPS, this card provides the compute headroom necessary for high-throughput batching, allowing developers to run multiple concurrent agentic workflows or high-resolution diffusion models without stalling the system.

The shift to 32GB of GDDR7 is the most significant upgrade for the AI community. This 33% increase in VRAM over the previous 24GB standard allows for larger model weights to reside entirely on-chip. The 512-bit memory bus ensures that the data path to these 32GB is never the bottleneck. However, practitioners must account for the 575W TDP. This is a high-density thermal load that requires a minimum 1000W PSU and a chassis capable of exhausting significant heat, especially in multi-GPU configurations common in AI development.

The RTX 5090 Founders Edition VRAM for large language models changes the math for local deployment. It moves the "sweet spot" of local inference from 7B-14B models up to the 30B-35B parameter range.

The 32GB buffer allows for the following configurations:

• 30B+ Parameter Models (e.g., Llama 3.1 8B, Command R, Qwen 2.5 32B): These run comfortably at 4-bit (Q4_K_M) or even 8-bit (Q8_0) quantization with ample room left for long context windows (32k+ tokens) and KV cache.
• 70B Parameter Models (e.g., Llama 3.1 70B, DeepSeek-V3): These are "tight" but viable. At a Q3_K_S or Q3_K_L quantization, a 70B model will occupy roughly 28-30GB of VRAM. This leaves very little room for context, making the 5090 the hardware for running 70B at Q3 (tight), though it is better suited for 30B+ at Q4 or Q5 comfortably.
• MoE Models (e.g., Mixtral 8x7B): Fits easily at Q4 or Q5, providing exceptionally high tokens per second due to the 1,792 GB/s bandwidth.

While actual performance depends on the backend (llama.cpp, vLLM, TensorRT-LLM), the NVIDIA GeForce RTX 5090 Founders Edition tokens per second on Llama 3.1 8B (FP16) can exceed 150-200 TPS. On larger 30B models at Q4, users can expect a highly fluid 40-60 TPS, making it ideal for real-time agentic interactions.

The 32GB VRAM is a game-changer for Flux.1, Stable Diffusion 3.5, and video generation models like Sora-scale local clones. It allows for high-resolution image generation and fine-tuning (LoRA) without the "Out of Memory" (OOM) errors common on 16GB or 24GB cards.

The RTX 5090 FE is the best AI chip for local deployment in 2025 for specific professional profiles:

• AI Agent Developers: For those building multi-agent systems where several models (a planner, a coder, and a critic) must reside in VRAM simultaneously, the 32GB capacity is the new baseline. It is the best AI GPU for agent training and iterative testing.
• Local LLM Power Users: Hobbyists running DeepSeek-R1 or Llama 3.1 locally will find the 5090 FE provides the smoothest experience for complex reasoning tasks that require larger parameter counts.
• Computer Vision Researchers: The 21,760 CUDA cores and high FP16 performance make it a powerhouse for training YOLO variants or fine-tuning vision transformers (ViT) on custom datasets.
• Enterprise Prototyping: Teams can use the 5090 to prototype RAG (Retrieval-Augmented Generation) pipelines locally before deploying to expensive cloud H100 instances, ensuring data privacy during the dev cycle.

When selecting the best hardware for local AI agents in 2025, the 5090 FE is often compared against its predecessor and the professional lineup.

The 4090 was the previous gold standard with 24GB VRAM. The 5090 offers 8GB more VRAM and nearly double the memory bandwidth. If your models were hitting the 24GB ceiling (common with Llama 3.1 70B or high-res Flux generation), the 5090 is a mandatory upgrade. For 8B models, the 4090 remains capable, but the 5090 provides a higher ceiling for future-proofing.

While the AMD RX 7900 XTX offers 24GB of VRAM at a much lower price point, NVIDIA remains the superior choice for NVIDIA gpus for AI development due to the maturity of the CUDA ecosystem. Most cutting-edge libraries (FlashAttention-2, AutoGPTQ, BitsAndBytes) are optimized for CUDA first. The 5090’s 32GB GDDR7 also outclasses the 7900 XTX’s 24GB GDDR6 in both capacity and speed, making the 5090 the clear winner for professional AI workloads.

The RTX 6000 Ada offers 48GB of VRAM, which is necessary for unquantized 70B models or large-batch training. However, the 5090 FE features the newer Blackwell architecture and much higher memory bandwidth at a fraction of the $6,800+ MSRP of the 6000 Ada. For practitioners who can work within a 32GB limit using quantization, the 5090 FE offers significantly better price-to-performance.