datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nanochat-rtx4070-sft-mixes
nanochat-rtx4070 SFT mixes
Eight SFT data mixes that were trained and evaluated on a single RTX 4070, and the results each one produced. Seven of them failed.
These are the actual independent variable behind the negative-results table in Bl4ckd09/nanochat-on-rtx4070. Every mix here was built deterministically, trained on the same frozen backbone with the same geometry and step count, and put through the same two-stage evaluation gate. Publishing only the winner would make the… See the full description on the dataset page: https://huggingface.co/datasets/Marcolini/nanochat-rtx4070-sft-mixes.windows-rtx-4060ti-8gb-moe-offload-bench-2026-05
RTX 4060 Ti 8GB — Multi-Model Benchmark (2026-05)
practitioner benchmarks on consumer hardware (8GB VRAM, 32GB RAM). 10 models tested, covering MoE expert offload, hybrid SSM architectures, dense models, MLA, dense partial GPU offload, and the 1B speed ceiling. all runs on the same physical rig, same methodology.
current leaderboard (decode tok/s at sweet spot)
model
active params
GGUF size
sweet spot tok/s
quality (6 tests)
architecture
Llama 3.2 1B
1.24B
771… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/windows-rtx-4060ti-8gb-moe-offload-bench-2026-05.rtx-5080-local-llm-coding-eval
Local LLM Coding Evaluation on an RTX 5080
Task-level coding eval results for local models on a single RTX 5080, published with the open task set used to produce them.
Task-level results for local coding models measured on a single retail RTX 5080.
Published together with the open task set (coding-eval-tasks-v1.json) so the evaluation can be re-run or extended rather than merely cited. An eval you cannot reproduce is an anecdote.
Source and license
Canonical page… See the full description on the dataset page: https://huggingface.co/datasets/iBlessi/rtx-5080-local-llm-coding-eval.windows-rtx-4060ti-8gb-bench-2026-05
Local LLM Bench — RTX 4060 Ti 8GB
Real practitioner benchmarks of open-source LLMs on consumer 8GB VRAM hardware.
Hardware
GPU: NVIDIA GeForce RTX 4060 Ti (8GB VRAM)
CPU: AMD Ryzen 5 7600X (6 cores, AM5)
RAM: 32GB DDR5-6000 CL36
Platform: Windows 11
Runtime: LM Studio (CUDA backend)
Methodology
All models loaded with:
Quantization: Q4_K_M (GGUF)
Context length: 16384 tokens
GPU offload: maximum (full GPU residency where it fits)
Temperature: 0.7
Top-p: 0.9… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/windows-rtx-4060ti-8gb-bench-2026-05.