RTX 4090 vs RTX 5090 for Local LLMs: Same 24GB-32GB but 30-50% Faster Inference

Two flagship NVIDIA cards, two very different stories for anyone running language models at home. The RTX 5090 versus RTX 4090 for local LLMs question is not really about whether a model fits, since both cards are generous on memory. It is about how fast the card can read that memory, and on raw bandwidth the 5090 pulls clearly ahead, turning the same quantised model into a noticeably snappier chat or coding assistant.

Quick Answer

The RTX 5090 carries 32GB of GDDR7 at roughly 1,792 GB/s, against the RTX 4090's 24GB of GDDR6X at about 1,008 GB/s. That bandwidth gap, near 78 percent on paper, translates into roughly 25 to 50 percent faster token generation on the same model, with the largest gains on bigger 32B-class models that the 5090's extra 8GB also lets you load more comfortably.

Why Bandwidth Decides Inference Speed

Local LLM inference is overwhelmingly memory-bound, not compute-bound. To produce each token the GPU must read every weight in the active model from VRAM, so the speed at which it can stream those weights sets the pace. The 5090's GDDR7 on a wider 512-bit bus moves data far faster than the 4090's GDDR6X on a 384-bit bus, and that is the single biggest reason the newer card feels quicker in chat.

This also explains why the gap varies by model size. A small 7B model is tiny relative to both cards' bandwidth, so real-world tests show the 5090 only around 6 to 7 percent ahead. Push up to a 32B model at four-bit quantisation and the bandwidth advantage stretches the lead out toward 50 percent or more, because there is simply more weight data to shuttle each token.

What Each Card Can Actually Hold

The 24GB ceiling on the 4090

The RTX 4090's 24GB comfortably runs a 7B or 13B model at full quality and handles a 32B model at four-bit quantisation with room for context. It starts to strain on 70B models, which usually need aggressive quantisation or splitting across two cards to fit at all.

The 32GB headroom on the 5090

The 5090's 32GB is the more meaningful capacity step than the speed bump for some users. That extra room lets you run a 32B model at a higher quality quantisation, keep a larger context window open, or fit a model that simply would not load on 24GB. For coding assistants that keep big chunks of a codebase in context, the headroom is the feature you feel most.

Real Token Speeds, Not Just Specs

Synthetic bandwidth numbers are one thing, generated tokens per second are what you live with. In published benchmarks a 70B model at four-bit quantisation reaches around 45 tokens per second on the 5090 versus roughly 36 on the 4090, a gap that grows as model size increases. On a 7B model the difference shrinks to single digits. For interactive use, anything past roughly 30 tokens per second already reads faster than most people, so the practical question is how large a model you want running at that comfortable speed, and the 5090 lets you go bigger before speed drops off.

Where the 8B jump matters most

The sharpest benchmark contrast appears at the smallest scale. Some tests put the 5090 near 200 tokens per second on an 8B model versus the 4090's roughly 130, a 67 percent jump. At that speed the bottleneck shifts entirely to how quickly you can read the output, not how quickly the card can produce it. That headroom becomes useful when you are running agentic pipelines that fire many short requests in succession.

If you are speccing a machine around this kind of workload, the AI and workstation PC range at Evetech is built around these high-VRAM cards with the cooling and power delivery they demand. Worth noting too that two 4090s can rival a single 5090 for total memory on very large models, though that doubles power draw and complexity.

Mixture-of-Experts Models: A New Variable

The strongest new coding models in 2026 are increasingly Mixture-of-Experts architectures. A 70B-plus MoE model activates only a fraction of its parameters each token, which means the bandwidth demand per token is far lower than the total size implies. Both the 4090 and the 5090 benefit from this shift, but the 5090's larger VRAM is more relevant now that these models need to keep a large parameter set resident in memory. A 70B-total MoE model that fits on the 5090 can generate surprisingly fast because only the active experts are read per token.

Power, Cost and the Honest Trade-off

The 5090 draws more power, around 575W against the 4090's 450W, so factor in a stronger power supply and better case airflow. On value the 4090 remains the cheaper card to buy and therefore cheaper per token generated, which keeps it a sensible pick if your models fit in 24GB and your current speeds are fine. The upgrade earns its keep when you regularly run 32B-plus models, want the 32GB headroom for context, or need that 45-plus tokens per second on heavier models. To see how the current builds stack up, the best-selling PCs at Evetech give a quick read on what local-AI machines are configured with right now.

Frequently Asked Questions

Do both cards run the same size models?

For most practical purposes yes, up to about 32B at four-bit quantisation. The 5090's extra 8GB lets you run those models at higher quality or with more context, and helps fit models the 24GB 4090 cannot quite hold.

How much faster is the 5090 in real use?

Expect roughly 25 to 50 percent more tokens per second on the same model, with the biggest gains on larger models. On small 7B models the difference shrinks to single digits because those are already far inside both cards' bandwidth.

Is the RTX 4090 still worth buying for local LLMs?

Yes, if your models fit in 24GB and your speeds satisfy you. It costs less to buy and therefore less per token, making it the value choice for many home setups that do not need the 5090's headroom.

What power supply does the RTX 5090 need?

Plan for a high-wattage, quality unit given the card's roughly 575W draw, plus good case airflow. The AI PC builds at Evetech are configured with appropriate power and cooling for these cards out of the box.

Does the extra VRAM matter more than the speed?

For coding assistants and long-context work, often yes. The 32GB lets you keep more of a project or conversation in memory at once, which can matter more day to day than raw token speed.