Agents-A1 35B-A3B on Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 GB)

Can the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 GB) run Agents-A1 35B-A3B?

Yes, it runs well (tight fit)

Runs well (tight fit). At Q4_K_M it needs about 24.3 GB of 26.2 GB usable, with an estimated speed of 27.7 tok/s.

Best quant: Q4_K_M
Generation speed: 27.7 tok/s
Prompt processing: 160 tok/s
First token (1K prompt): 6.4 s
Largest safe context: 101K

Best app to run it on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Usually the first to support new model architectures.

llama-server -hf mradermacher/Agents-A1-i1-GGUF:Q4_K_M

Ollama Runs the Hugging Face GGUF with ollama run hf.co/…
KoboldCpp

Want an agent? Agents-A1 35B-A3B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 Agents-A1 35B-A3B

Recommended for the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 GB): Q4_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q4_K_M file (opens in a new tab)

huggingface.co/mradermacher/Agents-A1-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Agents-A1-i1-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/InternScience/Agents-A1 (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 82Great quality (A) · 82 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4_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 welltight fit: How well it runs on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 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.	37.2 / 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.	24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	12.5 / 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 score	88.7 / 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 (32 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	16.9 GB	26.2 GB	43.4 tok/s	Runs great	60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	20.1 GB	26.2 GB	35.0 tok/s	Runs great	73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	24.3 GB	26.2 GB	27.7 tok/s	Runs welltight fit	82Great quality (A) · 82 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	27.8 GB	26.2 GB	19.7 tok/s	Runs wellover memory limit	84Great quality (A) · 84 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	31.7 GB	26.2 GB	—	Won't run	86Excellent quality (A+) · 86 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	40.1 GB	26.2 GB	—	Won't run	87Excellent quality (A+) · 87 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`F16`	73.1 GB	26.2 GB	—	Won't run	87Excellent quality (A+) · 87 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (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

87Excellent quality (A+) · 87 of 100: how capable the model is at F16, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell F16 · 98.2 tok/s · Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) Q6_K · 72.3 tok/s · Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (64 GB) Q6_K · 90.1 tok/s · Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (48 GB) Q5_K_M · 62.2 tok/s · Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q6_K · 179 tok/s · Runs well (tight fit)
87Excellent quality (A+) · 87 of 100: how capable the model is at F16, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) F16 · 51.3 tok/s · Runs great

Quick answers

How much memory does Agents-A1 35B-A3B need on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 32GB?

At Q4_K_M it needs about 24.3 GB. The Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 32GB has 26.2 GB usable.

How fast is Agents-A1 35B-A3B on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 32GB?

About 27.7 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Agents-A1 35B-A3B should I use on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 32GB?

Q4_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.