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Can the AMD Radeon RX 9070 XT run Jamba Reasoning 3B?

71 ptsGood (B) · 71 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.3/15

Yes, it runs well

Runs well. At F16 it needs about 7.8 GB of 16.0 GB usable, with an estimated speed of 49.6 tok/s.

Best quant
F16
Generation speed
49.6 tok/s
Prompt processing
18,852 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Jamba Reasoning 3B

Recommended for the AMD Radeon RX 9070 XT: 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/ai21labs/AI21-Jamba-Reasoning-3B-GGUF (opens in a new tab)

ollama run hf.co/ai21labs/AI21-Jamba-Reasoning-3B-GGUF:F16

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 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.19.8 / 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.11.3 / 15
Total71.1 / 10071 ptsGood (B) · 71 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.3/15

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

Every quantization on the AMD Radeon RX 9070 XT

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 2.7 GB 16.0 GB 347 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 13.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.9/15 GGUF files (opens in a new tab)
Q3_K_M 3.0 GB 16.0 GB 273 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15 GGUF files (opens in a new tab)
Q4_K_M 3.4 GB 16.0 GB 211 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.8/15 GGUF (opens in a new tab)
Q5_K_M 3.7 GB 16.0 GB 175 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 19.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 GGUF files (opens in a new tab)
Q6_K 4.0 GB 16.0 GB 147 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 19.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 GGUF files (opens in a new tab)
Q8_0 4.8 GB 16.0 GB 108 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.9/15 GGUF files (opens in a new tab)
F16 7.8 GB 16.0 GB 49.6 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.3/15 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.