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Can the NVIDIA GeForce RTX 4070 SUPER run Phi-4 Multimodal Instruct?

74 ptsGreat (A) · 74 of 100 points: Model quality 19.8/45, 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 8.2 GB of 12.0 GB usable, with an estimated speed of 56.5 tok/s.

Best quant
Q8_0
Generation speed
56.5 tok/s
Prompt processing
4,589 tok/s
First token (1K prompt)
0.3 s
Largest safe context
36K

Download Phi-4 Multimodal Instruct

Original weights · Hugging Face

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

huggingface.co/microsoft/Phi-4-multimodal-instruct (opens in a new tab)

No sign-in needed.

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

How the A 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.19.8 / 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
Total74.8 / 10074 ptsGreat (A) · 74 of 100 points: Model quality 19.8/45, 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 4070 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.5 GB 12.0 GB 182 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 13.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.7/15 All files (opens in a new tab)
Q3_K_M 5.0 GB 12.0 GB 144 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.6/15 All files (opens in a new tab)
Q4_K_M 5.7 GB 12.0 GB 111 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 All files (opens in a new tab)
Q5_K_M 6.2 GB 12.0 GB 91.8 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 19.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.0/15 All files (opens in a new tab)
Q6_K 6.8 GB 12.0 GB 77.1 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 19.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.2/15 All files (opens in a new tab)
Q8_0 8.2 GB 12.0 GB 56.5 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 All files (opens in a new tab)
F16 13.4 GB 12.0 GB 14.2 tok/s Runs with offload 53 ptsUsable (C) · 53 of 100 points: Model quality 19.8/45, Memory fit 10.0/25, Speed 8.9/15, Memory use 15.0/15 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.