NVIDIA RTX 5090 vs NVIDIA RTX 4090

Analysis

The NVIDIA RTX 5090 succeeds the RTX 4090 as NVIDIA's flagship consumer GPU, moving from Ada Lovelace to Blackwell architecture. For local AI inference, this is not a routine generational refresh: the 5090 brings a 78% memory bandwidth increase and 33% more VRAM, two numbers that directly determine which models you can run and how fast they generate tokens. At roughly $2,000, the 5090 lands at the same price tier the 4090 occupied at launch, making this a pure generation-over-generation value question.

The RTX 5090's 32 GB of GDDR7 on a 512-bit bus delivers 1,792 GB/s, nearly double the 4090's 1,008 GB/s from its 24 GB of GDDR6X on a 384-bit bus. In practice, the 5090 generates tokens roughly 1.3× to 1.5× faster on models both cards can hold in VRAM, short of the full 78% bandwidth gap because every layer also pays a fixed per-token launch cost that faster memory does not shrink: this site's calculator puts Llama 3.1 8B at Q4_K_M at 175.7 tok/s on the 5090 versus 116 on the 4090 (+51.5%), and GPT-OSS 20B at Q4_K_M at 286.7 versus 214.1 (+33.9%). llama.cpp's own CUDA scoreboard shows the same shape: 290 versus 186 tok/s on Llama 2 7B Q4_0 (+56%). The extra 8 GB is equally consequential: the 4090 tops out at running 30B-class models like Qwen 3-32B at Q4_K_M, with 70B models requiring CPU offload and its steep speed penalty. The 5090 handles those same 30B models at Q8_0 with room to spare and can fit some 70B models at Q4_K_M natively, a meaningful threshold, since 70B-class models (Llama 3.3 70B, Qwen 2.5-72B, DeepSeek-R1-Distill-Llama-70B) represent the current quality ceiling for open-weight local inference. Blackwell's 5th-gen Tensor Cores also add FP4 support, which inference engines like TensorRT-LLM and llama.cpp are increasingly targeting for higher throughput at equivalent quality, a capability Ada Lovelace simply lacks.

Bottom line: The RTX 5090 is the clear winner for local AI inference in absolute terms: more VRAM, dramatically more bandwidth, and a newer architecture with FP4 support. If you are building a new AI workstation in 2025, the 5090 is the obvious choice at roughly $2,000. The RTX 4090 remains defensible only if you find one at a significant discount on the used market and your workload stays comfortably within 24 GB: 7B–14B models at high quantization, or 30B models at Q4. For anyone running or planning to run 30B–70B class models locally, the 5090's extra VRAM and bandwidth justify the price difference on their own. The generational leap from Ada to Blackwell is one of the largest in NVIDIA's consumer GPU history for AI inference workloads.