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Can the NVIDIA GeForce RTX 5060 Ti 8GB run Granite Code 8B Instruct (128K)?

67 ptsGood (B) · 67 of 100 points: Model quality 14.0/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q5_K_M it needs about 7.9 GB of 8.0 GB usable, with an estimated speed of 56.4 tok/s.

Best quant
Q5_K_M
Generation speed
56.4 tok/s
Prompt processing
2,120 tok/s
First token (1K prompt)
0.5 s
Largest safe context
8K

Download Granite Code 8B Instruct (128K)

Recommended for the NVIDIA GeForce RTX 5060 Ti 8GB: Q5_K_M. 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 Q5_K_M file (opens in a new tab)

huggingface.co/QuantFactory/granite-8b-code-instruct-128k-GGUF (opens in a new tab)

ollama run hf.co/QuantFactory/granite-8b-code-instruct-128k-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/ibm-granite/granite-8b-code-instruct-128k (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.14.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total68.0 / 10067 ptsGood (B) · 67 of 100 points: Model quality 14.0/45, Memory fit 24.0/25, 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 5060 Ti 8GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.4 GB 8.0 GB 112 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 10.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 6.2 GB 8.0 GB 88.3 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 12.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 7.1 GB 8.0 GB 68.3 tok/s Tight fit 67 ptsGood (B) · 67 of 100 points: Model quality 13.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.9 GB 8.0 GB 56.4 tok/s Tight fit 67 ptsGood (B) · 67 of 100 points: Model quality 14.0/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 8.8 GB 8.0 GB 27.9 tok/s Runs with offload 51 ptsUsable (C) · 51 of 100 points: Model quality 14.3/45, Memory fit 10.0/25, Speed 12.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 10.7 GB 8.0 GB 12.3 tok/s Heavy offload 41 ptsPoor (D) · 41 of 100 points: Model quality 14.4/45, Memory fit 4.0/25, Speed 8.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 18.3 GB 8.0 GB 3.4 tok/s Heavy offload 37 ptsPoor (D) · 37 of 100 points: Model quality 14.4/45, Memory fit 4.0/25, Speed 4.4/15, Memory use 15.0/15 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.