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Can the NVIDIA GeForce RTX 3090 run Hermes 4.3 36B?

83 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 21.7 GB of 24.0 GB usable, with an estimated speed of 41.1 tok/s.

Best quant
Q3_K_M
Generation speed
41.1 tok/s
Prompt processing
709 tok/s
First token (1K prompt)
1.4 s
Largest safe context
16K

Download Hermes 4.3 36B

Recommended for the NVIDIA GeForce RTX 3090: Q3_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 Q3_K_M file (opens in a new tab)

huggingface.co/NousResearch/Hermes-4.3-36B-GGUF (opens in a new tab)

ollama run hf.co/NousResearch/Hermes-4.3-36B-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/NousResearch/Hermes-4.3-36B (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.29.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total83.9 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 3090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 18.4 GB 24.0 GB 52.2 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 24.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 21.7 GB 24.0 GB 41.1 tok/s Tight fit 83 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 26.0 GB 24.0 GB 14.6 tok/s Runs with offload 67 ptsGood (B) · 67 of 100 points: Model quality 33.8/45, Memory fit 10.0/25, Speed 9.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 29.6 GB 24.0 GB 6.8 tok/s Runs with offload 65 ptsGood (B) · 65 of 100 points: Model quality 34.5/45, Memory fit 10.0/25, Speed 6.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 33.6 GB 24.0 GB 4.2 tok/s Heavy offload 59 ptsGood (B) · 59 of 100 points: Model quality 35.2/45, Memory fit 4.0/25, Speed 4.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 42.3 GB 24.0 GB 2.2 tok/s Heavy offload 58 ptsGood (B) · 58 of 100 points: Model quality 35.6/45, Memory fit 4.0/25, Speed 3.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 76.2 GB 24.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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

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.