rtx-5090
Qwen3.8-27B-NVFP4-RTX5090Qwen3.8-27B-Uncensored-NVFP4-RTX5090Qwen3.8-27B-NVFP4-RTX5090-LMHead4Qwen3.8-27B-Uncensored-DSpark-RTX5090Huihui-Qwen3.8-27B-Abliterated-Gittensor-Style-NVFP4-RTX5090Qwen3.8-27B-NVFP4-RTX5090-LMHead4rtx5090_uncensored_optimized-qwen3-coder-30b-a3b-merged-ggufQwen3.8-27B-NVFP4-RTX5090-No-MTP
Datasets
All datasets matching “rtx-5090”rtx-5090-benchmarks
RTX 5090 LLM Benchmarks
Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with llm-bench-rig.
Quality Benchmarks
Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no lm-evaluation-harness dependency.
Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks.rtx5090-energy-benchmark
RTX 5090 LLM Energy Benchmark
First energy efficiency benchmark of 4-bit quantization on NVIDIA RTX 5090 (Blackwell architecture).
Key Finding
4-bit quantization increases energy consumption by up to 29% for models < 5B parameters.
The crossover point where quantization becomes beneficial is ~5B parameters.
Results
Model
FP16 Energy
4-bit Energy
Change
TinyLlama 1.1B
1,659 J/1k
2,098 J/1k
+26.5% 🔴
Qwen2 1.5B
2,411 J/1k
3,120 J/1k
+29.4% 🔴… See the full description on the dataset page: https://huggingface.co/datasets/hongpingzhang/rtx5090-energy-benchmark.rtx-5090-benchmarks
RTX 5090 LLM Benchmarks
Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with llm-bench-rig.
Quality Benchmarks
Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no lm-evaluation-harness dependency.
Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups… See the full description on the dataset page: https://huggingface.co/datasets/omegaprime669/rtx-5090-benchmarks.gr00t_rtx5090_testThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 60,
"total_frames": 17849,
"total_tasks": 1,
"total_videos": 60,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:60"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/akira-sasaki/gr00t_rtx5090_test.qwen3-coder-gb10-vs-rtx5090-benchmark
NVIDIA GB10 vs. GeForce RTX 5090 - Local LLM Inference Benchmark
Model: Qwen3-Coder-30B-A3B-InstructFormat: GGUF, Q4_K_M, 18.63 GBRuntime: LM Studio / llama.cppAuthor: Efehan A.Benchmark date: 5 August 2026
This repository contains a decode-focused local inference benchmark comparing an NVIDIA GB10 system with a Windows workstation containing two GeForce RTX 5090 GPUs. Telemetry shows that the inference workload was carried primarily by a single RTX 5090 (GPU 0), while GPU 1… See the full description on the dataset page: https://huggingface.co/datasets/mreltera/qwen3-coder-gb10-vs-rtx5090-benchmark.local-llms-benchmark-rtx5090
Local LLMs Benchmark — RTX 5090
Benchmark of 14 local language model configurations (9 distinct models, 27B–31B parameter range)
across 9 questions covering logical reasoning, Bayesian statistics, cognitive bias detection,
theoretical science, synthesis under contradiction, linguistic ambiguity, code optimization,
and AI ethics.
Hardware: RTX 5090 24GB | Intel Core Ultra 9 275HX | 64GB RAM | DebianInference backend: Ollama (Docker)Author: Francisco R. · LinkedIn
Full… See the full description on the dataset page: https://huggingface.co/datasets/Anodino/local-llms-benchmark-rtx5090.