DeepScaleR 1.5B Preview on Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (64 GB)

Can the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (64 GB) run DeepScaleR 1.5B Preview?

Yes, it runs great

Runs great. At Q5_K_M it needs about 6.5 GB of 54.4 GB usable, with an estimated speed of 42.5 tok/s.

Best quant: Q5_K_M
Generation speed: 42.5 tok/s
Prompt processing: 270 tok/s
First token (1K prompt): 3.8 s
Largest safe context: 128K

Best app to run it on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (64 GB)

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run deepscaler:1.5b

llama.cpp Usually the first to support new model architectures.
KoboldCpp

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download DeepScaleR 1.5B Preview

Recommended for the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (64 GB): Q5_K_M. 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 deepscaler:1.5b

Quantized files · GGUF

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

Download the Q5_K_M file (opens in a new tab)

huggingface.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF (opens in a new tab)

ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/agentica-org/DeepScaleR-1.5B-Preview (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 25Basic quality (D) · 25 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q5_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 great: How well it runs on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (64 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.	11.3 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.	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.	2.7 / 15
Match score	54.1 / 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 Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (64 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	5.9 GB	54.4 GB	84.6 tok/s	Runs great	18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	6.1 GB	54.4 GB	66.6 tok/s	Runs great	21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	6.3 GB	54.4 GB	51.5 tok/s	Runs great	24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	6.5 GB	54.4 GB	42.5 tok/s	Runs great	25Basic quality (D) · 25 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	6.6 GB	54.4 GB	35.7 tok/s	Runs great	25Basic quality (D) · 25 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	7.1 GB	54.4 GB	26.2 tok/s	Runs well	26Basic quality (D) · 26 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	8.7 GB	54.4 GB	12.1 tok/s	Runs well	26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (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

26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 44.0 tok/s · Runs great
26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 56.4 tok/s · Runs great
26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB F16 · 58.2 tok/s · Runs great
26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER F16 · 81.5 tok/s · Runs great
26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT F16 · 89.2 tok/s · Runs great
26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Ti 16GB F16 · 46.6 tok/s · Runs great

Quick answers

How much memory does DeepScaleR 1.5B Preview need on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 64GB?

At Q5_K_M it needs about 6.5 GB. The Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 64GB has 54.4 GB usable.

How fast is DeepScaleR 1.5B Preview on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 64GB?

About 42.5 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of DeepScaleR 1.5B Preview should I use on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 64GB?

Q5_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.