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Can the NVIDIA GeForce RTX 4060 run Zamba2 2.7B Instruct?

62 ptsGood (B) · 62 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15

Yes, it runs well

Runs well. At Q8_0 it needs about 4.2 GB of 8.0 GB usable, with an estimated speed of 63.9 tok/s.

Best quant
Q8_0
Generation speed
63.9 tok/s
Prompt processing
4,087 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Download Zamba2 2.7B Instruct

Original weights · Hugging Face

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

huggingface.co/Zyphra/Zamba2-2.7B-instruct (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the B 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.9.9 / 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.12.1 / 15
Total62.0 / 10062 ptsGood (B) · 62 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15

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

Every quantization on the NVIDIA GeForce RTX 4060

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 2.4 GB 8.0 GB 206 tok/s Runs well 53 ptsUsable (C) · 53 of 100 points: Model quality 6.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 All files (opens in a new tab)
Q3_K_M 2.7 GB 8.0 GB 162 tok/s Runs well 56 ptsUsable (C) · 56 of 100 points: Model quality 8.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.7/15 All files (opens in a new tab)
Q4_K_M 3.0 GB 8.0 GB 126 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 9.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.7/15 All files (opens in a new tab)
Q5_K_M 3.3 GB 8.0 GB 104 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 9.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.4/15 All files (opens in a new tab)
Q6_K 3.6 GB 8.0 GB 87.1 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 9.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15 All files (opens in a new tab)
Q8_0 4.2 GB 8.0 GB 63.9 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15 All files (opens in a new tab)
F16 6.7 GB 8.0 GB 29.4 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 9.9/45, Memory fit 25.0/25, Speed 12.9/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 4K 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.