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Can the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition run Llama 3.3 Nemotron Super 49B v1.5?

77Great quality (A) · 77 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q5_K_M it needs about 43.5 GB of 96.0 GB usable, with an estimated speed of 36.4 tok/s.

Best quant
Q5_K_M
Generation speed
36.4 tok/s
Prompt processing
1,588 tok/s
First token (1K prompt)
0.6 s
Largest safe context
128K

Best app to run it on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Llama 3.3 Nemotron Super 49B v1.5 in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

On Linux

vLLM

Inference server · runs on vLLM

  • The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
vllm serve nvidia/Llama-3_3-Nemotron-Super-49B-v1_5
  • LM Studio Point-and-click: search, download and chat in one window.
  • llama.cpp Usually the first to support new model architectures.

Want an agent? Llama 3.3 Nemotron Super 49B v1.5 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 Llama 3.3 Nemotron Super 49B v1.5

Recommended for the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: Q5_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 Q5_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:Q5_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)

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 77Great quality (A) · 77 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: 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.34.9 / 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.14.3 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.5 / 15
Match score84.7 / 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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

Quant Memory needed Available Speed Runs Quality Download
Q2_K 28.0 GB 96.0 GB 72.4 tok/s Runs great 56Good quality (B) · 56 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 32.5 GB 96.0 GB 57.0 tok/s Runs great 67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 38.5 GB 96.0 GB 44.1 tok/s Runs great 76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 43.5 GB 96.0 GB 36.4 tok/s Runs great 77Great quality (A) · 77 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 49.0 GB 96.0 GB 30.6 tok/s Runs great 79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 60.9 GB 96.0 GB 22.5 tok/s Runs well 80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 108 GB 96.0 GB 2.5 tok/s Very slowoffloads to RAM 80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Llama 3.3 Nemotron Super 49B v1.5 need on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

At Q5_K_M it needs about 43.5 GB. The NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB has 96.0 GB usable.

How fast is Llama 3.3 Nemotron Super 49B v1.5 on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

About 36.4 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Llama 3.3 Nemotron Super 49B v1.5 should I use on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

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.

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