Selecting the correct model format depends on your hardware capabilities and memory constraints.
BF16 (Brain Float 16) – Use if BF16 acceleration is available
A 16-bit floating-point format designed for faster computation while retaining good precision.
Provides similar dynamic range as FP32 but with lower memory usage.
Recommended if your hardware supports BF16 acceleration (check your device's specs).
Ideal for high-performance inference with reduced memory footprint compared to FP32.
📌 Use BF16 if:
✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
✔ You want higher precision while saving memory.
✔ You plan to requantize the model into another format.
📌 Avoid BF16 if:
❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.
F16 (Float 16) – More widely supported than BF16
A 16-bit floating-point high precision but with less of range of values than BF16.
Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
Slightly lower numerical precision than BF16 but generally sufficient for inference.
📌 Use F16 if:
✔ Your hardware supports FP16 but not BF16.
✔ You need a balance between speed, memory usage, and accuracy.
✔ You are running on a GPU or another device optimized for FP16 computations.
📌 Avoid F16 if:
❌ Your device lacks native FP16 support (it may run slower than expected).
❌ You have memory limitations.
Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.
📌 Use Quantized Models if:
✔ You are running inference on a CPU and need an optimized model.
✔ Your device has low VRAM and cannot load full-precision models.
✔ You want to reduce memory footprint while keeping reasonable accuracy.
📌 Avoid Quantized Models if:
❌ You need maximum accuracy (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)
These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.
IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
Use case: Best for ultra-low-memory devices where even Q4_K is too large.
Trade-off: Lower accuracy compared to higher-bit quantizations.
IQ3_S: Small block size for maximum memory efficiency.
Use case: Best for low-memory devices where IQ3_XS is too aggressive.
IQ3_M: Medium block size for better accuracy than IQ3_S.
Use case: Suitable for low-memory devices where IQ3_S is too limiting.
Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
Use case: Best for low-memory devices where Q6_K is too large.
Q4_0: Pure 4-bit quantization, optimized for ARM devices.
Use case: Best for ARM-based devices or low-memory environments.
Summary Table: Model Format Selection
Model Format
Precision
Memory Usage
Device Requirements
Best Use Case
BF16
Highest
High
BF16-supported GPU/CPUs
High-speed inference with reduced memory
F16
High
High
FP16-supported devices
GPU inference when BF16 isn't available
Q4_K
Medium Low
Low
CPU or Low-VRAM devices
Best for memory-constrained environments
Q6_K
Medium
Moderate
CPU with more memory
Better accuracy while still being quantized
Q8_0
High
Moderate
CPU or GPU with enough VRAM
Best accuracy among quantized models
IQ3_XS
Very Low
Very Low
Ultra-low-memory devices
Extreme memory efficiency and low accuracy
Q4_0
Low
Low
ARM or low-memory devices
llama.cpp can optimize for ARM devices
Included Files & Details
Nemotron-Research-Reasoning-Qwen-1.5B-bf16.gguf
Model weights preserved in BF16.
Use this if you want to requantize the model into a different format.
Best if your device supports BF16 acceleration.
Nemotron-Research-Reasoning-Qwen-1.5B-f16.gguf
Model weights stored in F16.
Use if your device supports FP16, especially if BF16 is not available.
Smallest Q4_K variant, using less memory at the cost of accuracy.
Best for very low-memory setups.
Nemotron-Research-Reasoning-Qwen-1.5B-q6_k.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q6_K .
Nemotron-Research-Reasoning-Qwen-1.5B-q8_0.gguf
Fully Q8 quantized model for better accuracy.
Requires more memory but offers higher precision.
Nemotron-Research-Reasoning-Qwen-1.5B-iq3_xs.gguf
IQ3_XS quantization, optimized for extreme memory efficiency.
Best for ultra-low-memory devices.
Nemotron-Research-Reasoning-Qwen-1.5B-iq3_m.gguf
IQ3_M quantization, offering a medium block size for better accuracy.
Suitable for low-memory devices.
Nemotron-Research-Reasoning-Qwen-1.5B-q4_0.gguf
Pure Q4_0 quantization, optimized for ARM devices.
Best for low-memory environments.
Prefer IQ4_NL for better accuracy.
🚀 If you find these models useful
❤ Please click "Like" if you find this useful!
Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks:
👉 Quantum Network Monitor
💬 How to test:
Choose an AI assistant type:
TurboLLM (GPT-4o-mini)
HugLLM (Hugginface Open-source)
TestLLM (Experimental CPU-only)
What I’m Testing
I’m pushing the limits of small open-source models for AI network monitoring, specifically:
Function calling against live network services
How small can a model go while still handling:
Automated Nmap scans
Quantum-readiness checks
Network Monitoring tasks
🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads):
✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs)
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!
Other Assistants
🟢 TurboLLM – Uses gpt-4o-mini for:
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)
🔵 HugLLM – Latest Open-source models:
🌐 Runs on Hugging Face Inference API
💡 Example commands to you could test:
"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
Final Word
I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.
If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
I'm also open to job opportunities or sponsorship.
Thank you! 😊
Nemotron-Research-Reasoning-Qwen-1.5B
🚀 The leading generalist reasoning model for cutting-edge research and development 🌟
Comparison between DeepSeek-R1-1.5B and Nemotron-Research-Reasoning-Qwen-1.5B
Introduction
Nemotron-Research-Reasoning-Qwen-1.5B is the world’s leading 1.5B open-weight model for complex reasoning tasks such as mathematical problems, coding challenges, scientific questions, and logic puzzles.
It is trained using the ProRL algorithm on a diverse and comprehensive set of datasets.
Our model has achieved impressive results, outperforming Deepseek’s 1.5B model by a large margin on a broad range of tasks, including math, coding, and GPQA.
This model is for research and development only.
ProRL: Prolonged Reinforcement Learning
ProRL is designed to enable extended RL training periods that facilitate deeper exploration of reasoning strategies.
It enables more than 2k training steps and scale the training data across diverse tasks—from traditional math and code tasks to STEM problems, logical puzzles, and instruction following, which, we hypothesize, are crucial for generalization.
Based on Group Relative Policy Optimization (GRPO), ProRL introduces three key techniques:
Mitigating Entropy Collapse
Decoupled clip and dynamic sampling policy optimization (DAPO)
KL regularization and reference policy reset
Using ProRL, we developed the world's best 1.5B reasoning model that significantly outperforms its base model, DeepSeek-R1-1.5B, and matches or even surpasses the performance of DeepSeek-R1-7B across a diverse range of benchmarks.
Notably, compared to DeepSeek-R1-1.5B, we achieve average pass@1 improvements of 14.7% on math benchmarks, 13.9% on coding, 54.8% on logic puzzles, 25.1% on STEM reasoning, and 18.1% on instruction-following tasks.
Table 1: Performance (pass@1) comparison for benchmarks across Math domain.
Model
AIME24
AIME25
AMC
Math
Minerva
Olympiad
Avg
DeepSeek-R1-Distill-Qwen-1.5B
28.54
22.71
62.58
82.90
26.38
43.58
44.45
DeepScaleR-1.5B
40.21
31.46
73.04
89.36
41.57
51.63
54.54
DeepSeek-R1-Distill-Qwen-7B
53.54
40.83
82.83
93.68
50.60
57.66
63.19
Nemotron-Research-Reasoning-Qwen-1.5B
48.13
33.33
79.29
91.89
47.98
60.22
60.14
Table 2: Performance (pass@1) comparison across benchmarks for Code. We abbreviate benchmarks names for condecontests (cc), codeforces (cf), humanevalplus (human), and livecodebench (LCB).
Model
apps
cc
cf
taco
human
LCB
Avg
DeepSeek-R1-Distill-Qwen-1.5B
20.95
16.79
14.13
8.03
61.77
16.80
23.08
DeepCoder-1.5B
30.37
23.76
21.70
13.76
73.40
22.76
30.96
DeepSeek-R1-Distill-Qwen-7B
42.08
32.76
33.08
19.08
83.32
38.04
41.39
Nemotron-Research-Reasoning-Qwen-1.5B
41.99
31.80
34.50
20.81
72.05
23.81
37.49
Table 3: Performance comparison on STEM reasoning (GPQA Diamond), instruction following (IFEval), and logic puzzles (Reasoning Gym) tasks. We also present results on OOD tasks: acre, boxnet, and game_of_life_halting (game).
Model
GPQA
IFEval
Reasoning
acre
boxnet
game
DeepSeek-R1-Distill-Qwen-1.5B
15.86
44.05
4.24
5.99
0.00
3.49
DeepSeek-R1-Distill-Qwen-7B
35.44
58.01
28.55
20.21
1.71
12.94
Nemotron-Research-Reasoning-Qwen-1.5B
41.78
66.02
59.06
58.57
7.91
52.29
License/Terms of Use
cc-by-nc-4.0
Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report security vulnerabilities or NVIDIA AI Concerns here.
Citation
If you find our dataset helpful, please cite the following paper:
@article{liu2025prorl,
author = {Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, Yi Dong},
title={ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models},
journal = {arXiv preprint},
year = {2025},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url={https://arxiv.org/abs/2505.24864},
}
