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Can the UP Squared Pro 7000 (4 GB) run Phi-2 2.7B?

11Basic quality (D) · 11 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 1/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. Phi-2 2.7B needs about 2.5 GB even at Q2_K; the UP Squared Pro 7000 (4 GB) has 2.0 GB usable.

Download Phi-2 2.7B

Recommended: Q2_K. 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.

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

ollama run phi:2.7b-chat-v2-q2_K

Quantized files · GGUF

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

Download the Q2_K file (opens in a new tab)

huggingface.co/TheBloke/phi-2-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/phi-2-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/microsoft/phi-2 (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 11Basic quality (D) · 11 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 1/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 Won't run
How well it runs on the UP Squared Pro 7000 (4 GB): 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.1.5 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Match score1.5 / 100

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

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the UP Squared Pro 7000 (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 2.5 GB 2.0 GB — Won't run 11Basic quality (D) · 11 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 1/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 2.8 GB 2.0 GB — Won't run 13Basic quality (D) · 13 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 1/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 3.1 GB 2.0 GB — Won't run 15Basic quality (D) · 15 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 3.4 GB 2.0 GB — Won't run 15Basic quality (D) · 15 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 3.7 GB 2.0 GB — Won't run 15Basic quality (D) · 15 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 4.4 GB 2.0 GB — Won't run 16Basic quality (D) · 16 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 7.0 GB 2.0 GB — Won't run 16Basic quality (D) · 16 of 100: how capable the model is at F16, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 2K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

Models that do run on the UP Squared Pro 7000 (4 GB)

Quick answers

How much memory does Phi-2 2.7B need on the UP Squared Pro 7000 4GB?

Even at Q2_K it needs about 2.5 GB; the UP Squared Pro 7000 4GB has 2.0 GB usable.

What can the UP Squared Pro 7000 4GB run instead?

LFM3 1.7B, PaddleOCR-VL 1.6 0.9B and MiniCPM-OCR 1.0 1.3B run best on it; the page lists more, each with its speed.

What hardware can run Phi-2 2.7B?

RTX 4070 SUPER 12GB, RTX 4060 8GB and RX 9070 XT 16GB run it; the page lists more, each with its speed.

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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