Which local LLMs can the GeForce RTX 3090 run?

Assumes 32 GB of DDR5-5600 system RAM, Windows with this GPU driving the display, 8k context and an f16 KV cache.

What to expect from the GeForce RTX 3090

The RTX 3090 has 24GB of VRAM and 936 GB/s of memory bandwidth, enough to keep dense models such as Gemma 4 31B and Qwen3.8 27B fully on the GPU at Q4_K_M, where both run great. Qwen3 32B fits too but runs well rather than great, because CanRun holds models that think before answering to a 1.5× stricter speed bar. Mixture-of-experts models are much faster: Qwen3 30B-A3B fits completely and runs great, and so does Qwen3.5 35B-A3B, with little room left at 8k; our page for Qwen3 30B-A3B (2507) on the GeForce RTX 3090 has the details. Nemotron 3.5 Lightning 30B-A3B just misses, though, and keeps some experts in system RAM.

70B is where 24GB stops. Llama 3.3 70B at Q4_K_M needs 45.2 GB, so about half of it would sit in system RAM; CanRun rates that too slow to use, and even the IQ4_XS file runs slowly. gpt-oss-120b does not load at all with 32GB of system RAM; with 64GB it runs slowly with its experts in RAM, a theoretical estimate (±30%). The calculator can also check two cards together.

The GeForce RTX 4090 has the same 24GB, so the same models fit; it is faster, and our data lists it at $2,755 street (as of 2026-08-09) against $1,050 street (as of 2026-08-09) for this card.

Specs

VRAM: 24 GB
Memory bandwidth: 936.0 GB/s
FP16 compute: 35.6 TFLOPS
Launch year: 2020
Price: $1,050 street (as of 2026-08-09)
Also written as: 3090, RTX3090

Verdicts at a glance

At Q4_K_M (or the closest available quant) with 8k context.

Runs great44 modelsRuns well5 modelsRuns slowly0 modelsWon't run10 models4 more models run at a lower quant.

How to read the verdicts

Runs great: Fully on the GPU at 20 tok/s or more
Runs well: Fully on the GPU at 8–20 tok/s; MoE experts in system RAM at 20 tok/s or more; or at least 90% on the GPU at 8 tok/s or more
Runs slowly: 2–8 tok/s; CPU-only; less than 90% on the GPU; or MoE experts in system RAM below 20 tok/s
Won't run: Does not fit, or under 2 tok/s

Every model on the GeForce RTX 3090

Dense models split between GPU and CPU slow down sharply — the CPU side sets the pace. MoE models that keep only their experts in system RAM degrade far more gently.

Scroll sideways to see every column.

59 models sorted by verdict and speed
Model	Verdict	Speed	Quant	Memory	Runs as	Context	Notes
MiniCPM5 1B	Runs great	est. 735.1 tok/s646.9–823.3calibrated estimate ±12%	Q4_K_M	1.9 / 24.0 GB	Full GPU	up to 128k	thinking mode
Llama 3.2 1B	Runs great	est. 607.5 tok/s534.6–680.5calibrated estimate ±12%	Q4_K_M	2.2 / 24.0 GB	Full GPU	up to 128k	—
EXAONE 4.0 1.2B	Runs great	est. 498.9 tok/s439.0–558.8calibrated estimate ±12%	Q4_K_M	2.5 / 24.0 GB	Full GPU	up to 64k	thinking mode
Ling-3.0 Tiny	Runs great	est. 377.2 tok/s264.1–490.4theoretical estimate ±30%	Q4_K_M	5.9 / 24.0 GB	Full GPU	up to 128k	thinking mode
LFM2.5 2.6B	Runs great	est. 363.1 tok/s254.2–472.1theoretical estimate ±30%	Q4_K_M	3.0 / 24.0 GB	Full GPU	up to 128k	thinking mode
HyperCLOVA X SEED 1.5B	Runs great	est. 360.9 tok/s317.6–404.2calibrated estimate ±12%	Q4_K_M	3.0 / 24.0 GB	Full GPU	up to 16k	—
MiniCPM5 2B	Runs great	est. 342.6 tok/s301.5–383.7calibrated estimate ±12%	Q4_K_M	2.9 / 24.0 GB	Full GPU	up to 128k	thinking mode
Gemma 4 E2B	Runs great	est. 269.1 tok/s188.4–349.8theoretical estimate ±30%	Q4_K_M	2.7 / 24.0 GB	Full GPU	up to 128k	thinking mode
Mellum2.1 12B-A2.5B Thinking	Runs great	est. 241.5 tok/s193.2–289.9calibrated estimate ±20%	Q4_K_M	9.2 / 24.0 GB	Full GPU	up to 128k	thinking mode
Spark-X2.5 4B	Runs great	est. 225.8 tok/s198.7–252.9calibrated estimate ±12%	Q4_K_M	4.1 / 24.0 GB	Full GPU	up to 256k	thinking mode
Llama 3.2 3B	Runs great	est. 221.4 tok/s194.8–248.0calibrated estimate ±12%	Q4_K_M	4.1 / 24.0 GB	Full GPU	up to 128k	—
Qwen3.5 4B	Runs great	est. 211.5 tok/s186.1–236.8calibrated estimate ±12%	Q4_K_M	4.2 / 24.0 GB	Full GPU	up to 256k	thinking mode
MiniCPM-V 4.7 35B-A3B	Runs great	est. 174.2 tok/s139.3–209.0calibrated estimate ±20%	Q4_K_M	22.2 / 24.0 GB	Full GPU	up to 64k	thinking mode
Qwen3.6 35B-A3B	Runs greatQwen3.6 35B-A3B verdict on the GeForce RTX 3090	est. 164.6 tok/s131.6–197.5calibrated estimate ±20%	Q4_K_M	22.4 / 24.0 GB	Full GPU	up to 32k	thinking mode
gpt-oss-20b	Runs greatgpt-oss-20b verdict on the GeForce RTX 3090	162.0 tok/s8k estimate 201.1 tok/s (160.9–241.3, calibrated estimate ±20%)measured (1 run, 2k context)	MXFP4	12.9 / 24.0 GB	Full GPU	up to 128k	thinking mode
Gemma 4 E4B	Runs great	est. 161.4 tok/s113.0–209.9theoretical estimate ±30%	Q4_K_M	4.3 / 24.0 GB	Full GPU	up to 128k	thinking mode
Kanana 1.5 15.7B-A3B	Runs great	est. 156.3 tok/s125.0–187.5calibrated estimate ±20%	Q4_K_M	12.5 / 24.0 GB	Full GPU	up to 32k	—
Qwen3.5 35B-A3B	Runs greatQwen3.5 35B-A3B verdict on the GeForce RTX 3090	est. 148.8 tok/s119.1–178.6calibrated estimate ±20%	Q4_K_M	22.9 / 24.0 GB	Full GPU	up to 32k	thinking mode
Qwen3 30B-A3B (2507)	Runs greatQwen3 30B-A3B (2507) verdict on the GeForce RTX 3090	est. 140.6 tok/s112.5–168.7calibrated estimate ±20%	Q4_K_M	20.4 / 24.0 GB	Full GPU	up to 32k	—
DeepSeek R1 Distill Llama 8B	Runs great	est. 109.3 tok/s96.2–122.4calibrated estimate ±12%	Q4_K_M	6.9 / 24.0 GB	Full GPU	up to 64k	thinking mode
Kanana 1.5 8B	Runs great	est. 109.3 tok/s96.2–122.4calibrated estimate ±12%	Q4_K_M	6.9 / 24.0 GB	Full GPU	up to 32k	—
Llama 3.1 8B	Runs greatLlama 3.1 8B verdict on the GeForce RTX 3090	est. 109.3 tok/s96.2–122.4calibrated estimate ±12%	Q4_K_M	6.9 / 24.0 GB	Full GPU	up to 64k	—
Gemma 4 26B-A4B	Runs greatGemma 4 26B-A4B verdict on the GeForce RTX 3090	108.0 tok/s8k estimate 134.9 tok/s (108.0–161.9, calibrated estimate ±20%)measured (1 run, 32k context)	Q4_K_M	18.3 / 24.0 GB	Full GPU	up to 128k	thinking mode
Nanbeige4.2 3B	Runs great	est. 106.9 tok/s74.9–139.0theoretical estimate ±30%	Q4_K_M	5.0 / 24.0 GB	Full GPU	up to 64k	thinking mode
Qwen3.5 9B	Runs greatQwen3.5 9B verdict on the GeForce RTX 3090	est. 106.7 tok/s93.9–119.5calibrated estimate ±12%	Q4_K_M	6.6 / 24.0 GB	Full GPU	up to 256k	thinking mode
DeepSeek R1 0528 Qwen3 8B	Runs great	est. 105.0 tok/s92.4–117.6calibrated estimate ±12%	Q4_K_M	7.1 / 24.0 GB	Full GPU	up to 64k	thinking mode
Qwen3 8B	Runs greatQwen3 8B verdict on the GeForce RTX 3090	est. 105.0 tok/s92.4–117.6calibrated estimate ±12%	Q4_K_M	7.1 / 24.0 GB	Full GPU	up to 32k	thinking mode
K2-Horizon 7B	Runs great	est. 96.4 tok/s84.8–107.9calibrated estimate ±12%	Q4_K_M	7.4 / 24.0 GB	Full GPU	up to 64k	thinking mode
Gemma 4 12B	Runs greatGemma 4 12B verdict on the GeForce RTX 3090	est. 90.3 tok/s79.5–101.2calibrated estimate ±12%	Q4_K_M	8.4 / 24.0 GB	Full GPU	up to 256k	thinking mode
Gemma 3 12B	Runs greatGemma 3 12B verdict on the GeForce RTX 3090	est. 83.6 tok/s73.6–93.6calibrated estimate ±12%	Q4_K_M	9.0 / 24.0 GB	Full GPU	up to 128k	—
HyperCLOVA X SEED Think 14B	Runs great	est. 64.3 tok/s45.0–83.5theoretical estimate ±30%	Q4_K_M	11.0 / 24.0 GB	Full GPU	up to 64k	thinking mode
Qwen3 14B	Runs greatQwen3 14B verdict on the GeForce RTX 3090	est. 63.4 tok/s55.7–71.0calibrated estimate ±12%	Q4_K_M	11.1 / 24.0 GB	Full GPU	up to 32k	thinking mode
DeepSeek R1 Distill Qwen 14B	Runs great	est. 61.8 tok/s54.4–69.2calibrated estimate ±12%	Q4_K_M	11.3 / 24.0 GB	Full GPU	up to 32k	thinking mode
Phi-4	Runs great	est. 61.1 tok/s53.7–68.4calibrated estimate ±12%	Q4_K_M	11.6 / 24.0 GB	Full GPU	up to 16k	—
Mistral Small 3.2 24B	Runs great	est. 41.8 tok/s36.8–46.8calibrated estimate ±12%	Q4_K_M	16.5 / 24.0 GB	Full GPU	up to 32k	—
Gemma 3 27B	Runs great	est. 38.0 tok/s33.5–42.6calibrated estimate ±12%	Q4_K_M	18.4 / 24.0 GB	Full GPU	up to 64k	—
Qwen3.6 27B	Runs great	est. 37.7 tok/s33.2–42.3calibrated estimate ±12%	Q4_K_M	17.8 / 24.0 GB	Full GPU	up to 64k	thinking mode
Muse Glimmer 30B	Runs great	est. 37.6 tok/s33.1–42.1calibrated estimate ±12%	Q4_K_M	17.8 / 24.0 GB	Full GPU	up to 128k	thinking mode
Qwen3.5 27B	Runs greatQwen3.5 27B verdict on the GeForce RTX 3090	est. 37.1 tok/s32.7–41.6calibrated estimate ±12%	Q4_K_M	17.8 / 24.0 GB	Full GPU	up to 64k	thinking mode
Qwen3.8 27B	Runs greatQwen3.8 27B verdict on the GeForce RTX 3090	est. 36.4 tok/s32.1–40.8calibrated estimate ±12%	Q4_K_M	18.2 / 24.0 GB	Full GPU	up to 64k	thinking mode
Gemma 4 31B	Runs greatGemma 4 31B verdict on the GeForce RTX 3090	est. 34.5 tok/s30.4–38.6calibrated estimate ±12%	Q4_K_M	20.1 / 24.0 GB	Full GPU	up to 32k	thinking mode
Granite 4.2 30B	Runs great	est. 33.0 tok/s29.0–36.9calibrated estimate ±12%	Q4_K_M	20.8 / 24.0 GB	Full GPU	up to 8k	thinking mode
EXAONE 4.0 32B	Runs great	est. 33.0 tok/s29.0–36.9calibrated estimate ±12%	Q4_K_M	20.8 / 24.0 GB	Full GPU	up to 32k	thinking mode
EXAONE 4.5 33B	Runs great	est. 31.8 tok/s28.0–35.6calibrated estimate ±12%	Q4_K_M	21.0 / 24.0 GB	Full GPU	up to 16k	thinking mode
Nemotron 3.5 Lightning 30B-A3B	Runs well	est. 122.0 tok/s85.4–158.7theoretical estimate ±30%	Q4_K_M	22.7 / 24.0 GB + 4.0 GB RAM	MoE experts in RAM	up to 256k	Not fully on the GPU — Runs great needs the whole model in GPU memory Try IQ4_XS: Runs great thinking mode
K2-Horizon MoVA 36B-A4B	Runs well	est. 79.2 tok/s55.5–103.0theoretical estimate ±30%	Q4_K_M	23.4 / 24.0 GB + 1.8 GB RAM	MoE experts in RAM	up to 32k	Not fully on the GPU — Runs great needs the whole model in GPU memory thinking mode
Qwen3 32B	Runs wellQwen3 32B verdict on the GeForce RTX 3090	est. 29.9 tok/s26.3–33.5calibrated estimate ±12%	Q4_K_M	22.6 / 24.0 GB	Full GPU	up to 8k	Try IQ4_XS: Runs great thinking mode
DeepSeek R1 Distill Qwen 32B	Runs well	est. 29.8 tok/s26.2–33.4calibrated estimate ±12%	Q4_K_M	22.7 / 24.0 GB	Full GPU	up to 8k	Q2_K (heavy quality loss): Runs great thinking mode
Nanbeige4.3 33B	Runs well	est. 29.1 tok/s25.6–32.6calibrated estimate ±12%	Q4_K_M	23.0 / 24.0 GB	Full GPU	up to 8k	Try IQ4_XS: Runs great thinking mode
Kolibri 1	Won't runTry IQ4_XS: Runs well	est. 68.5 tok/s47.9–89.0theoretical estimate ±30%	IQ4_XS	22.6 / 24.0 GB + 22.3 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory With 64 GB RAM: Runs well thinking mode
Qwen3.5 122B-A10B	Won't runTry UD-Q2_K_XL (heavy quality loss): Runs wellQwen3.5 122B-A10B verdict on the GeForce RTX 3090	est. 41.3 tok/s28.9–53.6theoretical estimate ±30%	UD-Q2_K_XL	22.5 / 24.0 GB + 21.8 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory With 64 GB RAM: Runs slowly thinking mode
Solar Open 100B	Won't runTry Q2_K (heavy quality loss): Runs slowly	est. 28.4 tok/s19.9–36.9theoretical estimate ±30%	Q2_K	22.7 / 24.0 GB + 17.8 GB RAM	MoE experts in RAM	up to 32k	Experts run from system RAM — Runs well needs 30 tok/s or more on this path With 64 GB RAM: Runs slowly thinking mode
Llama 3.3 70B	Won't runTry IQ4_XS: Runs slowlyLlama 3.3 70B verdict on the GeForce RTX 3090	est. 2.2 tok/s1.7–2.6calibrated estimate ±20%	IQ4_XS	24.0 / 24.0 GB + 17.8 GB RAM	Partial offload	up to 8k	—
DeepSeek V4 Flash 0731	Won't run	—	MXFP4	needs 155.1 GB	—	—	Needs about 155.1 GB; 23.4 GB of VRAM and 28.0 GB of RAM are free thinking mode
GLM-5.3 Flash	Won't run	—	Q4_K_M	needs 181.4 GB	—	—	Needs about 181.4 GB; 23.4 GB of VRAM and 28.0 GB of RAM are free thinking mode
gpt-oss-120b	Won't rungpt-oss-120b verdict on the GeForce RTX 3090	—	MXFP4	needs 63.6 GB	—	—	Needs about 63.6 GB; 23.4 GB of VRAM and 28.0 GB of RAM are free With 64 GB RAM: Runs slowly thinking mode
Laguna S 2.1	Won't run	—	Q4_K_M	needs 68.5 GB	—	—	Needs about 68.5 GB; 23.4 GB of VRAM and 28.0 GB of RAM are free With 64 GB RAM: Runs slowly thinking mode
Ling-3.0 Flash	Won't run	—	Q4_K_M	needs 75.4 GB	—	—	Needs about 75.4 GB; 23.4 GB of VRAM and 28.0 GB of RAM are free With 64 GB RAM: Runs well thinking mode
Solar Open 2 250B	Won't run	—	IQ4_XS	needs 136.5 GB	—	—	Needs about 136.5 GB; 23.4 GB of VRAM and 28.0 GB of RAM are free With 128 GB RAM: Runs slowly thinking mode

Models with a thinking mode spend extra tokens thinking, so their speed thresholds are 1.5× stricter (30 / 12 / 3 tok/s). With thinking turned off, a model may do one grade better.

Measured results on the GeForce RTX 3090

Public benchmarks we calibrate against. Their conditions (context, backend, flags) can differ from the estimates above.

Measured results on the GeForce RTX 3090
Model	Quant	Backend	Context	Prompt (tok/s)	Generation (tok/s)	Flags	Source	Measured
llama-2-7b	Q4_0	llama.cpp	512	5,175.0	158.2	—	github.com	2025-08-01
gpt-oss-20b	MXFP4	llama.cpp	2k	5,143.0	162.0	—	github.com	2025-08-20
Qwen3 8B	Q4_K_XL	llama.cpp	16k	—	87.5	—	hardware-corner.net	2026-08-09
Qwen3 30B-A3B (2507)	Q4_K_XL	llama.cpp	16k	—	113.8	—	hardware-corner.net	2026-08-09
Gemma 4 26B-A4B	Q4_K_M	ollama	32k	—	108.0	FA off	github.com	2026-04-07
Qwen3 8B	Q4_K_XL	llama.cpp	4k	4,050.0	115.3	CUDA	hardware-corner.net	2025-12-09
Qwen3 32B	Q4_K_XL	llama.cpp	4k	1,088.0	35.1	CUDA	hardware-corner.net	2025-12-09

Frequently asked questions

What is the largest model that runs entirely on the GeForce RTX 3090?

Qwen3.5 35B-A3B at Q4_K_M (a 22.63 GB file) fits entirely in 24 GB with 8k context, at est. 148.8 tok/s (119.1–178.6, calibrated estimate ±20%).

How many local LLMs run well on the GeForce RTX 3090?

At Q4_K_M with 8k context, out of 59 tracked models: 44 run great, 5 run well, 0 run slowly and 10 won't run.

Can the GeForce RTX 3090 run a 70B model like Llama 3.3 70B?

Runs slowly — Partial offload, IQ4_XS: est. 2.2 tok/s (1.7–2.6, calibrated estimate ±20%).

Speeds are estimates from memory bandwidth, calibrated against public benchmarks, and each one comes with an error band and a confidence label. Real results vary with drivers, backend, context length and thermals.