Nemotron 3.5 Super VL 120B-A12B on Intel Data Center GPU Max 1550

Can the Intel Data Center GPU Max 1550 run Nemotron 3.5 Super VL 120B-A12B?

Yes, it runs great

Runs great. At Q5_K_M it needs about 95.4 GB of 128 GB usable, with an estimated speed of 122 tok/s.

Best quant: Q5_K_M
Generation speed: 122 tok/s
Prompt processing: 4,767 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 1,024K

Best app to run it on the Intel Data Center GPU Max 1550

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Supports Intel Arc through SYCL and Vulkan.
Usually the first to support new model architectures.

llama-server -hf unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF:UD-Q5_K_XL

LM Studio Point-and-click: search, download and chat in one window.
Ollama Intel GPU support here is experimental (Vulkan).

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

Supports Intel Arc through SYCL and Vulkan.
Usually the first to support new model architectures.

llama-server -hf unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF:UD-Q5_K_XL

LM Studio Point-and-click: search, download and chat in one window.
Ollama Intel GPU support here is experimental (Vulkan).

Want an agent? Nemotron 3.5 Super VL 120B-A12B 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, 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 Nemotron 3.5 Super VL 120B-A12B

Recommended for the Intel Data Center GPU Max 1550: 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 nemotron-3.5-super:120b

Quantized files · GGUF

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

Download the Q5_K_M (UD-Q5_K_XL) files (opens in a new tab)

huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF

Original weights · Hugging Face

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

huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from NVIDIA. The weights themselves download from Hugging Face.

github.com/NVIDIA-NeMo/Nemotron (opens in a new tab)

NVIDIA’s Nemotron repo with recipes for the whole family.

git clone https://github.com/NVIDIA-NeMo/Nemotron

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

How it’s rated

Quality 90Excellent quality (A+) · 90 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 95/100 (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 Data Center GPU Max 1550: 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.	40.9 / 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.	15.0 / 15
Match score	95.9 / 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 Data Center GPU Max 1550

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	56.8 GB	128 GB	182 tok/s	Runs great	65Good quality (B) · 65 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q2_K_XL) · folder (opens in a new tab)
`Q3_K_M`	68.0 GB	128 GB	160 tok/s	Runs great	78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q3_K_XL) · folder (opens in a new tab)
`Q4_K_M`	83.0 GB	128 GB	138 tok/s	Runs great	89Excellent quality (A+) · 89 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q4_K_XL) · folder (opens in a new tab)
`Q5_K_M`	95.4 GB	128 GB	122 tok/s	Runs great	90Excellent quality (A+) · 90 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q5_K_XL) · folder (opens in a new tab)
`Q6_K`	109 GB	128 GB	108 tok/s	Runs welltight fit	92Excellent quality (A+) · 92 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q6_K_XL) · folder (opens in a new tab)
`Q8_0`	139 GB	128 GB	28.7 tok/s	Runs welloffloads to RAM	93Excellent quality (A+) · 93 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`F16`	256 GB	128 GB	—	Won't run	93Excellent quality (A+) · 93 of 100: how capable the model is at F16, whatever it runs on. Match score 12/100 (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

92Excellent quality (A+) · 92 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) Q6_K · 41.6 tok/s · Runs great
92Excellent quality (A+) · 92 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (256 GB) Q6_K · 42.4 tok/s · Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Q4_K_M · 113 tok/s · Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (128 GB) Q4_K_M · 41.4 tok/s · Runs well (tight fit)
89Excellent quality (A+) · 89 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX Spark N1X (6144-core GPU) (128 GB) Q4_K_M · 27.8 tok/s · Runs well
89Excellent quality (A+) · 89 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GB10 (128 GB) Q4_K_M · 25.5 tok/s · Runs well

Quick answers

How much memory does Nemotron 3.5 Super VL 120B-A12B need on the Intel Data Center GPU Max 1550 128GB?

At Q5_K_M it needs about 95.4 GB. The Intel Data Center GPU Max 1550 128GB has 128 GB usable.

How fast is Nemotron 3.5 Super VL 120B-A12B on the Intel Data Center GPU Max 1550 128GB?

About 122 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Nemotron 3.5 Super VL 120B-A12B should I use on the Intel Data Center GPU Max 1550 128GB?

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.