24GB VRAM

RTX 3090 — what LLMs can it run?

63 of 79 indexed models fit comfortably in 24GB at 4K context, each at the highest-quality quant that still leaves headroom.

32B · 17

RTX 3090 — what LLMs can it run?32B
ModelQuantEst. VRAMHeadroomtok/s
Seed-OSS 36B Instruct36BAWQ INT419.91 GB+4.1 GB55
Command R 35B35BGPTQ INT418.97 GB+5 GB55
Yi 1.5 34B Chat34BAWQ INT419 GB+5 GB52
Qwen3 32B Instruct32BAWQ INT418.22 GB+5.8 GB55
Qwen2.5 32B Instruct32BEXL2 3.5bpw15.96 GB+8 GB68
Qwen2.5-Coder 32B Instruct32BAWQ INT418.08 GB+5.9 GB52
DeepSeek-R1-Distill-Qwen-32B32BEXL2 3.5bpw15.96 GB+8 GB65
Qwen3 30B-A3B Instruct30B-A3BQ4_K_M19.73 GB+4.3 GB95
Qwen3-Coder 30B-A3B Instruct30B-A3BQ4_K_M19.73 GB+4.3 GB92
Qwen3-VL 30B-A3B Instruct30B-A3BQ4_K_M19.73 GB+4.3 GB95
Gemma 3 27B IT27BQ4_K_M19.49 GB+4.5 GB48
Gemma 2 27B Instruct27BQ4_K_M18.81 GB+5.2 GB48
Mistral Small 24B Instruct24BEXL2 4.65bpw15.26 GB+8.7 GB88
Devstral Small 1.1 24B24BQ6_K20.91 GB+3.1 GB48
Magistral Small 1.2 24B24BQ6_K20.91 GB+3.1 GB47
Codestral 22B22BQ4_K_M15.03 GB+9 GB58
GPT-OSS 20B21B MoEMXFP411.81 GB+12.2 GB195

14B · 17

RTX 3090 — what LLMs can it run?14B
ModelQuantEst. VRAMHeadroomtok/s
InternLM2 20B Chat20BQ5_K_M15.59 GB+8.4 GB68
DeepSeek-Coder-V2-Lite Instruct16BQ8_018.36 GB+5.6 GB118
DeepSeek-V2-Lite Chat16BQ4_K_M10.87 GB+13.1 GB142
StarCoder2 15B15BQ4_K_M10.19 GB+13.8 GB92
Qwen3 14B Instruct14BQ5_K_M11.65 GB+12.4 GB78
Qwen2.5 14B Instruct14BQ5_K_M11.73 GB+12.3 GB86
DeepSeek-R1-Distill-Qwen-14B14BEXL2 4.65bpw9.75 GB+14.3 GB128
Phi-4 14B14BQ5_K_M11.77 GB+12.2 GB78
Phi-3 Medium 14B Instruct14BQ6_K12.86 GB+11.1 GB88
Mistral Nemo 12B Instruct12BQ6_K11.14 GB+12.9 GB95
Gemma 3 12B IT12BQ5_K_M10.6 GB+13.4 GB92
Stable LM 2 12B Chat12BQ4_K_M8.35 GB+15.7 GB108
Jamba 1.5 Mini12BQ4_K_M8.15 GB+15.9 GB95
Llama 3.2 11B Vision Instruct11BQ8_012.9 GB+11.1 GB72
Solar 10.7B Instruct11BQ4_K_M7.6 GB+16.4 GB125
Falcon 3 10B Instruct10BQ4_K_M7.28 GB+16.7 GB118
Gemma 2 9B Instruct9BQ8_011.7 GB+12.3 GB108

7B · 19

RTX 3090 — what LLMs can it run?7B
ModelQuantEst. VRAMHeadroomtok/s
GLM-4-9B-Chat9BQ8_010.16 GB+13.8 GB105
Qwen3-VL 8B Instruct8BQ8_010.39 GB+13.6 GB108
Qwen2-VL 7B Instruct7BQ4_K_M5.49 GB+18.5 GB72
Granite 3.1 8B Instruct8BQ4_K_M5.88 GB+18.1 GB142
Qwen3 8B Instruct8BQ6_K7.64 GB+16.4 GB122
Llama 3.1 8B Instruct8BQ8_09.47 GB+14.5 GB118
Nous Hermes 3 Llama 3.1 8B8BEXL2 4.65bpw5.43 GB+18.6 GB232
Aya 23 8B8BQ4_K_M5.64 GB+18.4 GB145
OpenChat 3.6 8B8BEXL2 4.65bpw5.43 GB+18.6 GB228
DeepSeek-R1-Distill-Llama-8B8BQ5_K_M6.51 GB+17.5 GB128
InternLM2 7B Chat7BQ4_K_M5.45 GB+18.6 GB148
Qwen2.5 7B Instruct7BQ6_K6.77 GB+17.2 GB132
Qwen2.5-Coder 7B Instruct7BEXL2 4.65bpw4.87 GB+19.1 GB248
WizardLM-2 7B7BQ4_K_M5.07 GB+18.9 GB152
DeepSeek-R1-Distill-Qwen-7B7BEXL2 4.65bpw4.87 GB+19.1 GB210
OLMo 2 7B Instruct7BQ8_010.3 GB+13.7 GB125
Mistral 7B Instruct v0.37BQ6_K6.75 GB+17.3 GB135
Zephyr 7B Beta7BQ6_K6.75 GB+17.3 GB132
Gemma 3 4B IT4BQ8_05.36 GB+18.6 GB145

≤3B · 10

RTX 3090 — what LLMs can it run?≤3B
ModelQuantEst. VRAMHeadroomtok/s
Qwen3 4B Instruct4BQ6_K4.05 GB+20 GB145
Phi-4 Mini Instruct3.8BQ8_04.68 GB+19.3 GB262
Phi-3.5 Mini Instruct3.8BQ8_05.89 GB+18.1 GB255
Llama 3.2 3B Instruct3BQ8_04.05 GB+20 GB285
Qwen2.5 3B Instruct3BQ8_03.67 GB+20.3 GB290
Gemma 2 2B Instruct2BQ8_03.34 GB+20.7 GB320
Qwen3 1.7B Instruct1.7BQ8_02.39 GB+21.6 GB240
Qwen2.5 1.5B Instruct1.5BQ8_01.83 GB+22.2 GB410
Llama 3.2 1B Instruct1BQ8_01.51 GB+22.5 GB450
Qwen2.5 0.5B Instruct0.5BQ8_00.6 GB+23.4 GB540

How this list is built

Each row is the lowest-perplexity-loss quant of that model whose estimated total — weights plus KV cache at 4K context plus activation buffer — uses at most 88% of the card. That is the calculator's "green" threshold, so every row here has real headroom rather than only just fitting. Raise the context length and the list shortens; the calculator lets you check any combination directly.

63 of 79 indexed models fit comfortably in 24GB at 4K context, each at the highest-quality quant that still leaves headroom.