RTX 3090 vs RTX 4090 for local AI
This is one of the hardware comparisons on this site, and the answer depends on what kind of work you do: for bandwidth-bound text generation the two cards are closer than their generation gap suggests, while for compute-bound work like training and image sampling the newer card pulls clearly ahead. The RTX 3090 and the RTX 4090 both carry 24 GB, so unlike most GPU comparisons this one is not decided by capacity. See the hardware pages for how both fit into a wider build.
What fits
Both cards have 24 GB of GDDR6X. Anything that fits on one fits on the other, at the same quantization level, with the same context window headroom. There is no model class you can run on a 4090 that a 3090 cannot hold.
That makes 24 GB the number to plan around for either card:
- Mid-size text models run comfortably at common quantization levels, with room left for the KV cache.
- Large models need aggressive quantization, offloading part of the model to system RAM, or a second card.
- Mainstream image models fit easily, including a base model plus LoRA adapters held in VRAM at once.
- Video generation is the tight case on both, and is limited by clip length and resolution rather than by which of the two you own.
Both cards are also 24 GB per device in multi-GPU builds, so two of either gets you to the same aggregate pool.
Speed
The two sit in the same memory bandwidth class, roughly a terabyte per second of GDDR6X, with the newer card modestly ahead. Since token generation during inference is bandwidth-bound for a model that fits in memory, that is why the older card stays competitive for chat-style local LLM use and why the gap there is smaller than the two-generation-apart branding implies.
Compute is where they diverge. Ada Lovelace brings a large step in arithmetic throughput over Ampere, plus support for lower-precision data types that Ampere lacks. Everything compute-bound reflects that: prompt processing on long inputs, diffusion sampling for image and video, and fine-tuning runs. If your day is spent generating images or training adapters rather than chatting with a model, the newer card is the one to buy.
Power and heat also favour the newer card, which does more work per watt. The 3090 runs hot, in part because its memory modules sit on both sides of the board, and it wants deliberate airflow in a closed case. One thing the older card has that the newer one does not is an NVLink connector, which some multi-GPU training setups still make use of; consumer Ada and later dropped it.
Ecosystem
Both are CUDA cards with identical software stories. llama.cpp, Ollama and LM Studio treat them the same, and the image and video stacks do too. Ampere is old enough to be supported everywhere without special handling, and Ada is mature. Neither card will leave you hunting for a build that works.
The only practical asymmetry is that some newer kernels and low-precision paths target Ada and later, so an engine may offer an optimisation on the 4090 that silently falls back on the 3090. The fallback is correct, just slower.
Who should pick which
- You mainly run local LLMs for chat, drafting or coding help. The 3090 is the pragmatic pick. It gives you the same 24 GB and the same bandwidth class, which is what that workload actually consumes.
- You generate images or video, or you fine-tune. Take the 4090. These are compute-bound jobs where a generation of architecture is worth real time saved on every run.
- You are building a two-card machine for capacity. Two 3090s reach 48 GB and keep NVLink on the table, at the cost of a hotter, hungrier box. Decide by how much your power supply and case can honestly take.