NVIDIA RTX 4090: High-End GPU for Gaming & AI

• NVIDIA RTX 4090 is a high-end GPU based on Ada Lovelace architecture, featuring 24GB VRAM and 16,384 CUDA cores for versatile workloads.
• Recent research shows its ability to fine-tune large models via techniques like active gradient offloading and activation swapping, achieving up to 87 TFLOPS on GPT-3 175B.
• The RTX 4090 excels in high-performance computing, real-time rendering, and cost-effective AI inference, enabling applications from quantum simulation to video super-resolution.

NVIDIA RTX 4090 refers to a high-end consumer graphics processing unit (GPU) based on NVIDIA's Ada Lovelace architecture. With 24 GB of GDDR6X memory, up to 16,384 CUDA cores, and a peak memory bandwidth near 1 TB/s, the RTX 4090 is positioned at the intersection of gaming, AI research, and high-performance computing. Despite its consumer orientation, recent research demonstrates its competitiveness and efficiency across domains previously limited to data-center-class accelerators.

1. Hardware Architecture and Computational Characteristics

The RTX 4090 features 24 GB of GDDR6X VRAM on a 384-bit bus, delivering approximately 1,008 GB/s of sustained memory bandwidth. Its Ada Lovelace architecture provides up to 16,384 CUDA cores and fourth-generation Tensor cores, enabling peak compute of 82.6 TFLOPS (FP16 tensor) and ~1.3 TFLOPS (FP64). The device includes a 96 MB L2 cache and supports PCIe Gen4 x16 connectivity for high-throughput host interaction (Liao et al., 11 Mar 2024, Liu et al., 23 May 2025, Wang, 23 Sep 2024).

These architectural properties make the RTX 4090 highly suited for memory-intensive and compute-intensive workloads, even where the VRAM capacity is modest compared to server-class A100 (40–80 GB) or H100 SKUs.

2. Large Model Training and Fine-tuning Innovations

Recent advances enable fine-tuning of LLMs or other billion-scale architectures using a single RTX 4090 or small clusters thereof, circumventing historic GPU memory bottlenecks.

In LoHan ("Fuyou" system), 100B–175B-parameter model fine-tuning is achieved by dynamically paging model components—weights, activations, and optimizer states—between VRAM, host DRAM (256–768 GB), and fast SSD arrays (≥4×3.84 TB NVMe) (Liao et al., 11 Mar 2024). The key elements include:

• Active Gradient Offloading: Optimizer states and gradients are offloaded immediately to host/SSD, with CPU-based optimizer steps overlapped with GPU backward passes:

$$C_{\mathrm{offload}} = \sum_{i\in\text{layers}}\,\frac{S_{\mathrm{grad},i}}{BW_{\rm G\to C}}+\frac{S_{\mathrm{opt},i}}{BW_{\rm C\to S}}$$

• Holistic Traffic-Aware Activation Swapping: Activations are checkpointed and swapped using a pipeline overlapping GPU compute, PCIe, and NVMe transfers, scheduled under the constraint $$\sum_i S_{\rm act,i}^{\rm kept} \le M_{\rm GPU}^{\rm avail}$$ to maximize throughput and utilization.

Empirically, LoHan enables a single RTX 4090 to reach 87 TFLOPS (53% of theoretical peak) on GPT-3 175B with 16 batch size, outperforming ZeRO-Infinity by 2.3–3.5× and delivering 1.7× throughput-per-dollar over DGX-A100 clusters (Liao et al., 11 Mar 2024).

Separately, Block Coordinate Descent (BCD) frameworks partition parameters into blocks, updating one at a time (typically $M=3$). This reduces peak VRAM by $\approx$45% and lets a cluster of 32 RTX 4090s achieve equivalent or better accuracy (e.g., LLaMA-7B) at 2–3% the cost and with speedups up to $6.4\times$ per-iteration over OffLoad-Adam, and $1.4\times$ over A100 distributed schemes (Liu et al., 23 May 2025).

3. Efficient Large Model Inference and Quantization

RTX 4090's high INT/FP throughput is leveraged for fast, memory-efficient LLM inference, even for 100B+ parameter models.

PowerInfer (Song et al., 2023) adopts a hybrid GPU–CPU model: hot neurons (∼17% of all neurons, ~80% of activations) are preloaded onto VRAM; cold neurons are executed on CPU. This enables a 38% reduction in VRAM compared to full offloading, letting LLMs up to OPT-175B run at near-A100 speeds (up to 82% of A100 token throughput), and delivering 9.6–11.7× speedups (FP16) over baseline on a single 4090.

PoTPTQ (Wang et al., 16 Jul 2025) provides two-step power-of-two quantization, producing 2–3-bit weight formats optimized for warp-parallel, integer-dequantization on Ada Lovelace GPUs. On RTX 4090, this approach achieves a 1.63× reduction in dequant warp cycles (98→60 cycles), producing a 1.6× layer speedup and sustaining token rates from 130 to 210 tokens/s on LLaMA-13B, with minimal (<0.02) accuracy loss (WikiText-2 PPL = 5.48 at 3 bits).

4. General Matrix-Matrix Multiplication (GEMM) and Precision Emulation

Ozaki Scheme II (OZ2) (Ozaki et al., 10 Apr 2025) exploits INT8 Tensor Cores on the RTX 4090 to emulate IEEE FP64 matrix multiplication. By CRT decomposition into $s=14$ INT8 GEMMs followed by modular reconstruction, OZ2 attains 7.4–9.8 TFLOPS (8k×8k matrices)—approximately $16\times$ the performance observed for native FP64 DGEMM (0.62 TFLOPS)—at machine epsilon accuracy. The overhead breakdown shows GEMM calls dominate for large matrices, while conversion costs are subdominant beyond $p,q>2048$. OZ2’s efficiency stems from Ada Lovelace’s 660.6 TOPS INT8 tensor throughput, used here for high-accuracy FP64 emulation.

5. Scientific and High-Performance Applications

In quantum circuit simulation, QueenV2 (Wang, 23 Sep 2024) uses the 4090’s 16,384 CUDA cores, 1 TB/s bandwidth, and 96 MB L2 cache to achieve 137× speedup on individual gates versus cuQuantum, and 15× on 30-qubit QFT circuits. Gate fusion (IBM Qiskit) and all-in-cache streaming block kernels drive this step change, and the architecture’s memory-to-compute ratio facilitates breaking memory bottlenecks for state-vector evolution at scale.

4D-Rotor Gaussian Splatting (Duan et al., 5 Feb 2024) demonstrates the RTX 4090’s real-time rendering capacity for dynamic scene novel-view synthesis. A tile-based CUDA engine and FP16/Tensor Core-accelerated matrix ops enable 583 FPS at $1352\times1014$; memory-bound bottlenecks are alleviated via structure-of-arrays layouts and shared-memory tiling.

LiftVSR (Wang et al., 10 Jun 2025) uses 4 RTX 4090s to train advanced diffusion-based video super-resolution models, matching or exceeding the performance of systems previously requiring $>8\times$ A100s. Mixed-precision training, latent-space modeling, and segment-wise temporal attention fit within the 24GB VRAM limit, with batch size and architectural tradeoffs shaped by these constraints.

6. Cost, Throughput, and Practical Deployment

The RTX 4090 offers exceptional cost-effectiveness for both training and inference. LoHan achieves 0.35 TFLOPS per \$1,000 on 175B GPT-3, compared to 0.20 TFLOPS per \$1,000 on DGX-A100, and a $5M tokens/dollar$ ROI, 1.7× that of DGX-A100 setups (Liao et al., 11 Mar 2024). For distributed training, BCD on 4090 clusters attains $\approx98$% cost reduction relative to A100 full-parameter baselines of equivalent model size and quality (Liu et al., 23 May 2025).

PowerInfer’s memory-parsimonious design allows standard desktops with an RTX 4090 (24 GB VRAM) and a high-end CPU (i9-13900K, PCIe 4.0) to rival A100-based LLM deployment at a fraction of the hardware cost (Song et al., 2023).

7. Limitations and Configuration Caveats

VRAM remains a bottleneck: 24 GB imposes constraints on batch size and sequence length (Liao et al., 11 Mar 2024, Liu et al., 23 May 2025). High host RAM (≥256 GB) and multi-SSD arrays are critical to fully realize system throughput, especially with aggressive tensor movement and optimizer offloads. PCIe Gen4 (x16) bandwidth must be saturated to avoid IO contention, and mixed-precision or quantization is often required for large-scale workloads.

A plausible implication is that, although the RTX 4090 bridges the gap toward data-center class workloads, practitioners must carefully co-optimize software scheduling, tensor partitioning, and system architecture to fully unlock its capabilities across differing application domains.