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Can the NVIDIA GeForce RTX 4090 run Granite 4.0 H 350M?

47 ptsUsable (C) · 47 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.4/15

Yes, it runs well

Runs well. At F16 it needs about 2.5 GB of 24.0 GB usable, with an estimated speed of 854 tok/s.

Best quant
F16
Generation speed
854 tok/s
Prompt processing
174,918 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Granite 4.0 H 350M

Recommended for the NVIDIA GeForce RTX 4090: F16. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run granite4:350m-h

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

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/ibm-granite/granite-4.0-h-350m-GGUF (opens in a new tab)

ollama run hf.co/ibm-granite/granite-4.0-h-350m-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/ibm-granite/granite-4.0-h-350m (opens in a new tab)

No sign-in needed.

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

How the C 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.5.4 / 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.2.4 / 15
Total47.8 / 10047 ptsUsable (C) · 47 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.4/15

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

Every quantization on the NVIDIA GeForce RTX 4090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 2.0 GB 24.0 GB 5,977 tok/s Runs well 45 ptsUsable (C) · 45 of 100 points: Model quality 3.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.9/15 GGUF (opens in a new tab)
Q3_K_M 2.0 GB 24.0 GB 4,705 tok/s Runs well 46 ptsUsable (C) · 46 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.9/15 GGUF (opens in a new tab)
Q4_K_M 2.0 GB 24.0 GB 3,639 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 5.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.0/15 GGUF (opens in a new tab)
Q5_K_M 2.1 GB 24.0 GB 3,006 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 5.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.0/15 GGUF (opens in a new tab)
Q6_K 2.1 GB 24.0 GB 2,525 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 5.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.0/15 GGUF (opens in a new tab)
Q8_0 2.2 GB 24.0 GB 1,853 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.1/15 GGUF (opens in a new tab)
F16 2.5 GB 24.0 GB 854 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.4/15 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.