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Can the Intel Arc Pro B70 run Llama 3.3 Nemotron Super 49B v1.5?

78 ptsGreat (A) · 78 of 100 points: Model quality 30.2/45, Memory fit 24.0/25, Speed 9.1/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 29.3 GB of 32.0 GB usable, with an estimated speed of 14.8 tok/s.

Best quant
Q3_K_M
Generation speed
14.8 tok/s
Prompt processing
412 tok/s
First token (1K prompt)
2.5 s
Largest safe context
15K

Download Llama 3.3 Nemotron Super 49B v1.5

Recommended for the Intel Arc Pro B70: Q3_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 Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 (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)

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

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

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

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.30.2 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.9.1 / 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
Total78.4 / 10078 ptsGreat (A) · 78 of 100 points: Model quality 30.2/45, Memory fit 24.0/25, Speed 9.1/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Intel Arc Pro B70

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 24.8 GB 32.0 GB 18.8 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 10.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 29.3 GB 32.0 GB 14.8 tok/s Tight fit 78 ptsGreat (A) · 78 of 100 points: Model quality 30.2/45, Memory fit 24.0/25, Speed 9.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 35.3 GB 32.0 GB 6.9 tok/s Runs with offload 65 ptsGood (B) · 65 of 100 points: Model quality 34.2/45, Memory fit 10.0/25, Speed 6.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 40.3 GB 32.0 GB 3.9 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 34.9/45, Memory fit 10.0/25, Speed 4.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 45.8 GB 32.0 GB 2.6 tok/s Heavy offload 58 ptsGood (B) · 58 of 100 points: Model quality 35.6/45, Memory fit 4.0/25, Speed 3.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 57.7 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
F16 105 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (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

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.