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Can the NVIDIA RTX A400 run Phi-4 Multimodal Instruct?

36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q3_K_M it needs about 4.6 GB of 4.0 GB usable, with an estimated speed of 24.9 tok/s.

Best quant
Q3_K_M
Generation speed
24.9 tok/s
Prompt processing
295 tok/s
First token (1K prompt)
3.5 s
Largest safe context
3K

Best app to run it on the NVIDIA RTX A400

On Windows

Docker Model Runner

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

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Handy if you already work in Docker.
docker model run hf.co/microsoft/Phi-4-multimodal-instruct

On Linux

Docker Model Runner

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

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Handy if you already work in Docker.
docker model run hf.co/microsoft/Phi-4-multimodal-instruct

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-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 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_K_M, 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 welloffloads to RAM
How well it runs on the NVIDIA RTX A400: 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.16.6 / 45
Memory fitFull marks when everything sits in GPU 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.11.8 / 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
Match score53.5 / 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 A400

Quant Memory needed Available Speed Runs Quality Download
Q2_K 4.1 GB 4.0 GB 34.1 tok/s Runs welloffloads to RAM 30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 4.6 GB 4.0 GB 24.9 tok/s Runs welloffloads to RAM 36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 5.3 GB 4.0 GB 18.2 tok/s Slowheavy RAM offload 41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 5.8 GB 4.0 GB 14.5 tok/s Slowheavy RAM offload 42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 6.4 GB 4.0 GB 11.9 tok/s Slowheavy RAM offload 43Fair quality (C) · 43 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 7.8 GB 4.0 GB 8.4 tok/s Slowheavy RAM offload 44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 13.0 GB 4.0 GB — Won't run 44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (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-4 Multimodal Instruct need on the NVIDIA RTX A400 4GB?

At Q3_K_M it needs about 4.6 GB. The NVIDIA RTX A400 4GB has 4.0 GB usable.

How fast is Phi-4 Multimodal Instruct on the NVIDIA RTX A400 4GB?

About 24.9 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Phi-4 Multimodal Instruct should I use on the NVIDIA RTX A400 4GB?

Q3_K_M. 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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