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Can the NVIDIA GeForce RTX 3050 Ti Laptop GPU run Jamba Reasoning 3B?

74 ptsGreat (A) · 74 of 100 points: Model quality 19.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 3.1 GB of 4.0 GB usable, with an estimated speed of 60.8 tok/s.

Best quant
Q5_K_M
Generation speed
60.8 tok/s
Prompt processing
1,193 tok/s
First token (1K prompt)
0.9 s
Largest safe context
256K

Download Jamba Reasoning 3B

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/ai21labs/AI21-Jamba-Reasoning-3B-GGUF (opens in a new tab)

ollama run hf.co/ai21labs/AI21-Jamba-Reasoning-3B-GGUF

Original weights · Hugging Face

Full-precision files from AI21 Labs, for fine-tuning, vLLM, Transformers.

huggingface.co/ai21labs/AI21-Jamba-Reasoning-3B (opens in a new tab)

No sign-in needed.

Links checked 5 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.19.2 / 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
Total74.2 / 10074 ptsGreat (A) · 74 of 100 points: Model quality 19.2/45, 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 3050 Ti Laptop GPU

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 2.1 GB 4.0 GB 121 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 13.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.0/15 GGUF files (opens in a new tab)
Q3_K_M 2.4 GB 4.0 GB 95.2 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.7/15 GGUF files (opens in a new tab)
Q4_K_M 2.8 GB 4.0 GB 73.7 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 3.1 GB 4.0 GB 60.8 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 19.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q6_K 3.4 GB 4.0 GB 51.1 tok/s Tight fit 73 ptsGreat (A) · 73 of 100 points: Model quality 19.6/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q8_0 4.2 GB 4.0 GB 33.7 tok/s Runs with offload 58 ptsGood (B) · 58 of 100 points: Model quality 19.8/45, Memory fit 10.0/25, Speed 13.8/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 7.2 GB 4.0 GB 8.9 tok/s Heavy offload 45 ptsUsable (C) · 45 of 100 points: Model quality 19.8/45, Memory fit 4.0/25, Speed 7.1/15, 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.