Check my GPU

on

Can the NVIDIA GeForce RTX 5090 run InternVL3 78B?

57 ptsUsable (C) · 57 of 100 points: Model quality 28.0/45, Memory fit 10.0/25, Speed 4.9/15, Memory use 15.0/15

Yes, with part of the model in system RAM

Runs with offload. At Q3_K_M it needs about 43.3 GB of 32.0 GB usable, with an estimated speed of 4.2 tok/s.

Best quant
Q3_K_M
Generation speed
4.2 tok/s
Prompt processing
764 tok/s
First token (1K prompt)
1.3 s
Largest safe context
—

Download InternVL3 78B

Recommended for the NVIDIA GeForce RTX 5090: Q3_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/InternVL3-78B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/InternVL3-78B-GGUF:Q3_K_M

Original weights · Hugging Face

Full-precision files from Shanghai AI Lab, for fine-tuning, vLLM, Transformers.

huggingface.co/OpenGVLab/InternVL3-78B (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the C 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.28.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.4.9 / 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
Total57.8 / 10057 ptsUsable (C) · 57 of 100 points: Model quality 28.0/45, Memory fit 10.0/25, Speed 4.9/15, 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 5090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 36.2 GB 32.0 GB 10.2 tok/s Runs with offload 55 ptsUsable (C) · 55 of 100 points: Model quality 23.3/45, Memory fit 10.0/25, Speed 7.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 43.3 GB 32.0 GB 4.2 tok/s Runs with offload 57 ptsUsable (C) · 57 of 100 points: Model quality 28.0/45, Memory fit 10.0/25, Speed 4.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 52.7 GB 32.0 GB 2.3 tok/s Heavy offload 54 ptsUsable (C) · 54 of 100 points: Model quality 31.6/45, Memory fit 4.0/25, Speed 3.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 60.6 GB 32.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q6_K 69.2 GB 32.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q8_0 88.0 GB 32.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 10.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (UD-Q8_K_XL) · folder (opens in a new tab)
F16 162 GB 32.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 10.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K 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.