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Can the NVIDIA RTX 5000 Ada Generation Laptop GPU run Phi-3.5 Vision Instruct?

38Basic quality (D) · 38 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 9.0 GB of 16.0 GB usable, with an estimated speed of 86.7 tok/s.

Best quant
Q8_0
Generation speed
86.7 tok/s
Prompt processing
7,391 tok/s
First token (1K prompt)
0.3 s
Largest safe context
25K

Best app to run it on the NVIDIA RTX 5000 Ada Generation Laptop GPU

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/microsoft/Phi-3.5-vision-instruct

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/microsoft/Phi-3.5-vision-instruct
  • vLLM For one person, llama.cpp-based apps are about as fast and use smaller GGUF files.
  • SGLang For one person, llama.cpp-based apps are about as fast and use smaller GGUF files.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Phi-3.5 Vision Instruct

Original weights · Hugging Face

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

huggingface.co/microsoft/Phi-3.5-vision-instruct (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 38Basic quality (D) · 38 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the NVIDIA RTX 5000 Ada Generation Laptop GPU: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.17.1 / 45
Memory fitFull marks when everything sits in GPU 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.13.0 / 15
Match score70.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA RTX 5000 Ada Generation Laptop GPU

Quant Memory needed Available Speed Runs Quality Download
Q2_K 6.3 GB 16.0 GB 280 tok/s Runs great 26Basic quality (D) · 26 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 6.7 GB 16.0 GB 220 tok/s Runs great 31Basic quality (D) · 31 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 7.2 GB 16.0 GB 170 tok/s Runs great 36Basic quality (D) · 36 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 7.6 GB 16.0 GB 141 tok/s Runs great 36Basic quality (D) · 36 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 8.0 GB 16.0 GB 118 tok/s Runs great 37Basic quality (D) · 37 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 9.0 GB 16.0 GB 86.7 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 12.9 GB 16.0 GB 40.0 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Phi-3.5 Vision Instruct need on the NVIDIA RTX 5000 Ada Generation Laptop GPU 16GB?

At Q8_0 it needs about 9.0 GB. The NVIDIA RTX 5000 Ada Generation Laptop GPU 16GB has 16.0 GB usable.

How fast is Phi-3.5 Vision Instruct on the NVIDIA RTX 5000 Ada Generation Laptop GPU 16GB?

About 86.7 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Phi-3.5 Vision Instruct should I use on the NVIDIA RTX 5000 Ada Generation Laptop GPU 16GB?

Q8_0. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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