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Can the NVIDIA T400 run Nemotron Content Safety Reasoning 4B?

74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (tight fit)

Very slow (tight fit). At Q4_K_M it needs about 3.7 GB of 4.0 GB usable and makes one decision in about 9.8 s.

Best quant
Q4_K_M
Time per decision
9.8 s
Decisions per second
0.1/s
Input assumed
512 tokens
Returns
Reasoned verdict

Best app to run it on the NVIDIA T400

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open Nemotron Content Safety Reasoning 4B 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

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open Nemotron Content Safety Reasoning 4B in LM Studio

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

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 Content Safety Reasoning 4B

Recommended for the NVIDIA T400: 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/DevQuasar/nvidia.Nemotron-Content-Safety-Reasoning-4B-GGUF (opens in a new tab)

ollama run hf.co/DevQuasar/nvidia.Nemotron-Content-Safety-Reasoning-4B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/nvidia/Nemotron-Content-Safety-Reasoning-4B (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 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (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 Very slowtight fit
How well it runs on the NVIDIA T400: 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.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.33.3 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.24.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.1.5 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.10.0 / 15
Match score68.9 / 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 NVIDIA T400

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 3.7 GB 4.0 GB 9.8 s Very slowtight fit 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 4.1 GB 4.0 GB 11.5 s Very slowoffloads to RAM 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 4.6 GB 4.0 GB 13.6 s Very slowoffloads to RAM 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 5.6 GB 4.0 GB 18.5 s Very slowheavy RAM offload 78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 9.7 GB 4.0 GB 39.0 s Very slowheavy RAM offload 78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Times are for one request with a 512-token input and about 160 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does Nemotron Content Safety Reasoning 4B need on the NVIDIA T400 4GB?

At Q4_K_M it needs about 3.7 GB. The NVIDIA T400 4GB has 4.0 GB usable.

How fast is Nemotron Content Safety Reasoning 4B on the NVIDIA T400 4GB?

It makes one decision in about 9.8 s at Q4_K_M, an estimate from the hardware and the size of the model.

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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