Which LLMs Fit in 24 GB of VRAM?

By TechCompare · Updated · How we check

48 of the 108 models in the calculator fit in 24 GB at Q4_K_M with 8K context. Granite Switch 4.1 30B ranks first at 22.5 GB, and Qwen3 32B is the largest dense model that fits, at 23.9 GB.

24 GB is the go-to size for running 27B to 32B models at home, on cards like the RTX 3090 and RTX 4090. This page sizes every model in the LLM VRAM Calculator for it, at Q4_K_M and Q8_0, with 8K and 32K tokens of context. Models that fit at one of those setups make the table, and the closest misses come after it.

The totals include the weights, the KV cache and a 10% allowance for the runtime. Each number links to the calculator with the same setup, where you can try a different context or KV cache type.

Models that fit in 24 GB of VRAM

Best pick first, by size and release date. Each cell is the total memory the model needs. Tight means under 10% of the memory is left. Pick a number to open it in the calculator.

ModelQ4_K_M8K contextQ4_K_M32K contextQ8_08K contextQ8_032K context
Granite Switch 4.1 30B29.99B, Oct 202622.5 GBTight29.6 GBShort 5.6 GB37.4 GBShort 13.4 GB44.5 GBShort 20.5 GB
Muse Glimmer 30B30B, Aug 202620.4 GBFits20.7 GBFits35.2 GBShort 11.2 GB35.6 GBShort 11.6 GB
Qwen 3.8 27B27.5B, Aug 202619.2 GBFits21.0 GBFits32.9 GBShort 8.9 GB34.6 GBShort 10.6 GB
Gemma 4 31B (Dense)30.7B, Mar 202622.3 GBTight24.5 GBShort 0.5 GB37.5 GBShort 13.5 GB39.7 GBShort 15.7 GB
Qwen3.5 27B27.78B, Feb 202619.4 GBFits21.2 GBFits33.2 GBShort 9.2 GB35.0 GBShort 11.0 GB
Xing4.0 29B-A4B (MoE)31.2B, 4B active, Sep 202621.4 GBFits22.6 GBTight36.8 GBShort 12.8 GB38.1 GBShort 14.1 GB
Hy-Memory 30B-A3B (MoE)30.06B, 3B active, Oct 202621.1 GBFits23.8 GBTight36.0 GBShort 12.0 GB38.6 GBShort 14.6 GB
Nemotron 3.5 Lightning 30B (MoE)31.6B, 3B active, Aug 202621.4 GBFits21.5 GBFits37.0 GBShort 13.0 GB37.2 GBShort 13.2 GB
Qwen3 32B32B, Apr 202523.9 GBTight31.0 GBShort 7.0 GB39.7 GBShort 15.7 GB46.8 GBShort 22.8 GB
LFM2.5 24B-A2B (MoE)24B, 2B active, Oct 202616.3 GBFits16.9 GBFits28.2 GBShort 4.2 GB28.8 GBShort 4.8 GB
Gemma 4 12B12B, May 20268.6 GBFits9.1 GBFits14.6 GBFits15.0 GBFits
Gemma 4 26B (MoE A4B)25.2B, 4B active, Mar 202617.4 GBFits17.9 GBFits29.8 GBShort 5.8 GB30.4 GBShort 6.4 GB
Granite Switch 4.1 8B8.78B, Oct 20267.4 GBFits11.9 GBFits11.8 GBFits16.2 GBFits
Apertus v1.5 8B8.84B, Sep 20267.2 GBFits10.7 GBFits11.5 GBFits15.1 GBFits
K2 Horizon 7B9B, Sep 20267.4 GBFits11.4 GBFits11.9 GBFits15.9 GBFits

Just too big at Q4_K_M with 8K context

Totals include the weights, the KV cache at FP16 with a batch size of 1, and a 10% allowance for the runtime.

What fits at Q4_K_M

This is the 27B to 32B class. Granite Switch 4.1 30B ranks first at 22.5 GB, a tight fit. Qwen3 32B is the largest dense model that fits. At 23.9 GB it leaves almost nothing spare, so run it on a card that isn't driving your screens.

For more headroom, Muse Glimmer 30B needs 20.4 GB and Qwen 3.8 27B needs 19.2 GB. The order in the table follows the model finder's best pick, which favors newer models of a similar size. Use it to choose a few to try.

32K context on 24 GB

At 32K, the dense 31B and 32B models drop out. Qwen3 32B needs 31.0 GB and Gemma 4 31B (Dense) needs 24.5 GB. Muse Glimmer 30B still fits at 20.7 GB, and Qwen 3.8 27B at 21.0 GB.

The gap is in the KV cache. Qwen3 32B caches every token on all 64 of its layers, which takes 8.6 GB at 32K. Muse Glimmer 30B keeps a full cache on one layer in four and only the last 2,048 tokens on the rest, so its cache is 0.6 GB. For long documents on 24 GB, models built like that are the ones to look at.

Q8_0 on 24 GB

At Q8_0, the 27B to 32B models no longer fit. Qwen 3.8 27B needs 32.9 GB there. The largest dense model at Q8_0 is Qwen3 14B, at 18.7 GB with 8K and 23.2 GB with 32K.

That makes 24 GB a Q4_K_M card for most people. A 27B to 32B model at Q4_K_M usually beats a 14B model at Q8_0. Use Q8_0 for the smaller models when you want them as close to the original as you can get.

Just over 24 GB

A few models miss by less than 1 GB. Nanbeige4.3 33B needs 24.2 GB, DeepSeek R1 Distill 32B 24.2 GB and Qwen3.5 35B (MoE) 24.4 GB.

For Nanbeige4.3 33B, a q8_0 KV cache is enough. It halves the 1.8 GB cache, at a small cost in quality, and you can set it in the calculator. Dropping to Q3_K_M saves more: Nanbeige4.3 33B needs 19.8 GB there.

Cards with 24 GB

The RTX 3090, RTX 3090 Ti and RTX 4090 have 24 GB, and so does AMD's RX 7900 XTX. Token speed depends mostly on memory bandwidth, and the RTX 3090 and RTX 4090 are close there, so they write text at a similar pace. The RTX 4090 pulls ahead when reading long prompts, which leans on compute.

A Mac with 32 GB fits this list too. The calculator gives it 24 GB for models and keeps 8 GB for macOS and your apps, as a planning estimate. For two 24 GB cards together, see the 48 GB list.

Other memory sizes

Frequently asked questions

What's the best LLM for an RTX 3090 or 4090?
Granite Switch 4.1 30B ranks first for 24 GB at Q4_K_M with 8K context, at 22.5 GB. If you want 32K context, Muse Glimmer 30B fits at 20.7 GB. Try a few from the top of the table on your own tasks.
Can 24 GB of VRAM run a 32B model?
Yes, at Q4_K_M with a short context. Qwen3 32B needs 23.9 GB at 8K, but 31.0 GB at 32K, which doesn't fit. DeepSeek R1 Distill 32B misses even at 8K, by 0.2 GB.
Can 24 GB of VRAM run a 70B model?
Not on the card. Llama 3.3 70B needs 50.0 GB at Q4_K_M and 40.7 GB at Q3_K_M, both with 8K. With a second 24 GB card it gets close, which the 48 GB list covers.
What fits on a 32 GB Mac?
The same list as this page. The calculator gives a 32 GB Mac 24 GB for models, after 8 GB for macOS and your apps. Mac memory has less bandwidth than an RTX 3090 or 4090, so the same model writes text more slowly there.
Should I use Q8_0 on a 24 GB card?
For models up to 14B, it fits and keeps more detail. Qwen3 14B at Q8_0 needs 23.2 GB even at 32K. For anything bigger, Q4_K_M is the practical choice on 24 GB, and a bigger model at Q4_K_M usually gives better answers than a smaller one at Q8_0.