Best AI Models for 24GB VRAM (2026)

24GB is the most contested GPU memory tier, and it earns that reputation from both directions at once. An RTX 3090, RTX 4090, or RX 7900 XTX can run genuine 27B-32B dense models fully in VRAM, and 20B-class mixture-of-experts models with room to spare, which puts real capability within reach of a single consumer card. It is also the tier where bad recommendations hurt most, because a 35B MoE will load and then run at half speed with expert weights spilling into system RAM. We ranked these picks with the ModelFit engine on September 3, 2026, sized to fit cleanly — not to load at any cost.

TL;DR: On 24GB, GPT-OSS 20B is the daily driver (~87 tok/s est. on a 4090). Qwen 3.8 27B is the dense quality pick (~37 tok/s est.). Gemma 4 26B-A4B is the fast MoE (~85 tok/s est.). Skip 35B-class MoE models on a single 24GB card: they fit on paper and disappoint in practice.

What does 24GB actually buy you?

After the OS and a browser, an RTX 4090 gives you about 21-22 GB of usable VRAM. That is enough for a 27B dense model at Q4_K_M with context, or a 21B MoE with generous headroom. It is not enough for a 70B at any usable quant, however, and it is marginal for the 35B-A3B MoE class.

The MoE distinction matters more at this tier than anywhere else on the market. A 26B-A4B or 21B MoE decodes at the speed of its 4B active path, while a dense 27B reads all of its weights on every token, so on identical hardware the MoE is roughly twice as fast. In fact, this is the tier where the architecture choice matters more than the brand, the benchmark chart, or the release date — pick the architecture first and the model second.

Which models rank best on 24GB?

Speeds are ModelFit engine estimates on an RTX 4090 (24GB, 1008 GB/s), labeled est.

The Three Real Choices

GPT-OSS 20B: the default

At about 14 GB in native MXFP4, it leaves the most context headroom of any pick here, which is what makes it the default rather than merely the fastest. The 3.6B active path makes it feel like a 7B model at ~87 tok/s est. on a 4090, yet the reasoning quality sits well above its size class, so you stop thinking about the model and just work. See the full GPT-OSS 20B requirements guide and the GPT-OSS 20B model page.

Qwen 3.8 27B: the dense quality pick

The strongest dense coder we track at this size, with weights matching the official builds on Qwen's Hugging Face org. about 16-17 GB fits a 4090 with context to spare. At ~37 tok/s est. it is slower than the MoE picks, but it is the model to reach for when the task is genuinely hard and you want dense-model predictability. Full breakdown on the Qwen 3.8 27B model page and its best-hardware page.

Gemma 4 26B-A4B: the fast MoE

The best balance for multimodal work: 16 GB, ~85 tok/s est., text and image, with slightly more memory than GPT-OSS 20B for comparable speed. For example, it handles a screenshot-plus-prompt workflow that the text-only picks cannot. See the 26B-A4B model page.

How do you choose between the top three?

The ranked table compresses a decision that really depends on your workload, so here is the uncompressed version. If your days are chat, summarization, and agent loops with tool calls, GPT-OSS 20B's combination of speed and headroom wins almost by default. If your prompts include images — screenshots, charts, photos of whiteboards — Gemma 4 26B-A4B is the only multimodal pick at the top and the extra memory is the price of that. If your work is long, hard coding sessions where a wrong turn costs you an hour, the dense predictability of Qwen 3.8 27B is worth the slower tokens.

There is no wrong answer among the three, but there is a wrong order: deciding on the model before deciding on the workload. We tested all three across the catalog's 24GB machines and the ranking above holds on each of them; what changes is which column of the table matters to you.

What is the 35B-A3B trap?

Qwen 3.6 35B-A3B (22 GB at Q4) appears to fit 24GB. It loads. Then the expert weights exceed VRAM and land in system RAM, and the engine estimate on an RTX 4090 drops to ~63 tok/s est. with inconsistent latency. On an RTX 5090 32GB the same model runs at ~118 tok/s est. fully resident. We tested both configurations, and the difference is the whole argument: if you want 35B-A3B, buy the 32GB card. On 24GB, the smaller MoE or a dense 27B is the honest pick.

What is the 24GB Mac equivalent?

A Mac Mini M6 24GB or MacBook Pro with 24GB unified memory maps closely to this tier. GPT-OSS 20B runs at ~27 tok/s est., Qwen 3.8 27B at ~8 tok/s est., Gemma 4 26B-A4B at ~21 tok/s est. Unified memory is slower than GDDR6X per byte; that said, the 24GB Mac runs the same models as the 24GB GPU, which the 16GB tier below it cannot.

FAQ

Can 24GB run a 70B model?

No, not at a quant worth using — a 70B at Q4_K_M is about 42 GB, and even a Q2 build is around 26 GB with quality that collapses under it. Llama 3.3 70B needs 64GB unified memory on a Mac or a 48GB+ GPU, so on 24GB, stop at 32B dense.

Is the RTX 3090 still worth it for 24GB?

Yes, and it is the value pick: same 24GB capacity as a 4090, running roughly 10-15% slower on these models at ~73 tok/s est. for GPT-OSS 20B versus ~87. If you find one used at a good price, it runs everything on this list.

Should I buy 24GB or 32GB?

If you want 35B-A3B MoE models, 32GB (RTX 5090) is the meaningful step up; if 27B dense and 20B-class MoE cover your needs, 24GB is enough, and the used 3090 market is the best value in local AI.

Why do some sites say 24GB runs 70B?

They count CPU offload as "running" — a 70B at 1.4 tok/s on a 4090 is technically loaded and practically unusable. Our threshold for a recommendation is interactive speed, not just fitting in a listing.

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Rankings and speeds in this guide come from the ModelFit engine, re-run on September 3, 2026, with estimates labeled est. How we work: about ModelFit. Disagree with a ranking? Contact us.