Granite 3.2 8B Instruct on AMD Radeon RX 9060 XT 8GB
Can the AMD Radeon RX 9060 XT 8GB run Granite 3.2 8B Instruct?
Yes, with little memory to spare
Tight fit. At Q4_K_M it needs about 7.3 GB of 8.0 GB usable, with an estimated speed of 41.4 tok/s.
- Best quant
Q4_K_M- Generation speed
- 41.4 tok/s
- Prompt processing
- 3,885 tok/s
- First token (1K prompt)
- 0.3 s
- Largest safe context
- 12K
Download Granite 3.2 8B Instruct
Recommended for the AMD Radeon RX 9060 XT 8GB: Q4_K_M. 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 granite3.2:8b-instruct-q4_K_MQuantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the Q4_K_M file (opens in a new tab)
huggingface.co/bartowski/ibm-granite_granite-3.2-8b-instruct-GGUF (opens in a new tab)
ollama run hf.co/bartowski/ibm-granite_granite-3.2-8b-instruct-GGUF:Q4_K_MOriginal weights · Hugging Face
Full-precision files from IBM, for fine-tuning, vLLM, Transformers.
huggingface.co/ibm-granite/granite-3.2-8b-instruct (opens in a new tab)
How the A grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 19.7 / 45 | |
|---|---|---|
| Memory fit | 24.0 / 25 | |
| Speed | 15.0 / 15 | |
| Memory use | 15.0 / 15 | |
| Total | 73.7 / 100 | 73 ptsGreat (A) · 73 of 100 points: Model quality 19.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 |
Every quantization on the AMD Radeon RX 9060 XT 8GB
| Quant | Memory needed | Available | Speed | Fit | Grade · points | Download |
|---|---|---|---|---|---|---|
Q2_K | 5.6 GB | 8.0 GB | 68.0 tok/s | Runs well | 69 ptsGood (B) · 69 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q3_K_M | 6.3 GB | 8.0 GB | 53.5 tok/s | Runs well | 72 ptsGreat (A) · 72 of 100 points: Model quality 17.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q4_K_M | 7.3 GB | 8.0 GB | 41.4 tok/s | Tight fit | 73 ptsGreat (A) · 73 of 100 points: Model quality 19.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q5_K_M | 8.1 GB | 8.0 GB | 31.8 tok/s | Runs with offload | 58 ptsGood (B) · 58 of 100 points: Model quality 20.1/45, Memory fit 10.0/25, Speed 13.4/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q6_K | 9.0 GB | 8.0 GB | 19.3 tok/s | Runs with offload | 55 ptsUsable (C) · 55 of 100 points: Model quality 20.5/45, Memory fit 10.0/25, Speed 10.4/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q8_0 | 11.0 GB | 8.0 GB | 10.1 tok/s | Heavy offload | 47 ptsUsable (C) · 47 of 100 points: Model quality 20.7/45, Memory fit 4.0/25, Speed 7.5/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
F16 | 18.7 GB | 8.0 GB | 3.2 tok/s | Heavy offload | 43 ptsUsable (C) · 43 of 100 points: Model quality 20.7/45, Memory fit 4.0/25, Speed 4.2/15, 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
- 75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT
- 75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER
- 75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER
- 75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti
- 75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080
- 75 ptsGreat (A) · 75 of 100 points: Model quality 20.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 SUPER
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.