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Can the NVIDIA GeForce RTX 4080 SUPER run Agents-A1 4B?

82 ptsGreat (A) · 82 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At F16 it needs about 10.7 GB of 16.0 GB usable, with an estimated speed of 46.7 tok/s.

Best quant
F16
Generation speed
46.7 tok/s
Prompt processing
8,278 tok/s
First token (1K prompt)
0.3 s
Largest safe context
164K

Download Agents-A1 4B

Recommended for the NVIDIA GeForce RTX 4080 SUPER: F16. 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 F16 file (opens in a new tab)

huggingface.co/bartowski/InternScience_Agents-A1-4B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/InternScience_Agents-A1-4B-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/InternScience/Agents-A1-4B (opens in a new tab)

No sign-in needed.

Links checked 7 days 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.27.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 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
Total82.0 / 10082 ptsGreat (A) · 82 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, 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 4080 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 3.5 GB 16.0 GB 327 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.0/15 GGUF (opens in a new tab)
Q3_K_M 3.9 GB 16.0 GB 257 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.6/15 GGUF (opens in a new tab)
Q4_K_M 4.4 GB 16.0 GB 199 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q5_K_M 4.9 GB 16.0 GB 164 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 26.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.1/15 GGUF (opens in a new tab)
Q6_K 5.4 GB 16.0 GB 138 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 GGUF (opens in a new tab)
Q8_0 6.5 GB 16.0 GB 101 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.3/15 GGUF (opens in a new tab)
F16 10.7 GB 16.0 GB 46.7 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab) 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.