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Can the NVIDIA GeForce RTX 3090 run LLaVA 1.5 13B?

64 ptsGood (B) · 64 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 19.3 GB of 24.0 GB usable, with an estimated speed of 43.8 tok/s.

Best quant
Q8_0
Generation speed
43.8 tok/s
Prompt processing
1,920 tok/s
First token (1K prompt)
0.5 s
Largest safe context
4K

Download LLaVA 1.5 13B

Recommended for the NVIDIA GeForce RTX 3090: Q8_0. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run llava:13b-v1.5-q8_0

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Original weights · Hugging Face

Full-precision files from LLaVA, for fine-tuning, vLLM, Transformers.

huggingface.co/liuhaotian/llava-v1.5-13b (opens in a new tab)

No sign-in needed.

Links checked 1 week ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.9.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total64.9 / 10064 ptsGood (B) · 64 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 3090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 10.5 GB 24.0 GB 141 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 6.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.1/15
Q3_K_M 11.7 GB 24.0 GB 111 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 8.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.2/15
Q4_K_M 13.3 GB 24.0 GB 86.1 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 9.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15
Q5_K_M 14.6 GB 24.0 GB 71.1 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 9.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15
Q6_K 16.1 GB 24.0 GB 59.7 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 9.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15
Q8_0 19.3 GB 24.0 GB 43.8 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15
F16 31.9 GB 24.0 GB 4.2 tok/s Runs with offload 39 ptsPoor (D) · 39 of 100 points: Model quality 9.9/45, Memory fit 10.0/25, Speed 4.8/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.