Gemma 3 270M on AMD Radeon RX 9070 XT
Can the AMD Radeon RX 9070 XT run Gemma 3 270M?
Yes, it runs well
Runs well. At F16 it needs about 2.0 GB of 16.0 GB usable, with an estimated speed of 588 tok/s.
- Best quant
F16- Generation speed
- 588 tok/s
- Prompt processing
- 223,430 tok/s
- First token (1K prompt)
- 0.3 s
- Largest safe context
- 32K
Download Gemma 3 270M
Recommended for the AMD Radeon RX 9070 XT: 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 gemma3:270m-it-fp16Quantized 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/unsloth/gemma-3-270m-it-GGUF (opens in a new tab)
ollama run hf.co/unsloth/gemma-3-270m-it-GGUF:F16Original weights · Hugging Face
Full-precision files from Google, for fine-tuning, vLLM, Transformers.
Official repo · GitHub
Reference code, inference scripts and docs from Google. The weights themselves download from Hugging Face.
github.com/google-deepmind/gemma (opens in a new tab)
git clone https://github.com/google-deepmind/gemmaHow the C grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 2.7 / 45 | |
|---|---|---|
| Memory fit | 25.0 / 25 | |
| Speed | 15.0 / 15 | |
| Memory use | 2.8 / 15 | |
| Total | 45.5 / 100 | 45 ptsUsable (C) · 45 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.8/15 |
Every quantization on the AMD Radeon RX 9070 XT
| Quant | Memory needed | Available | Speed | Fit | Grade · points | Download |
|---|---|---|---|---|---|---|
Q2_K | 1.5 GB | 16.0 GB | 4,115 tok/s | Runs well | 44 ptsUsable (C) · 44 of 100 points: Model quality 1.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.2/15 | GGUF (opens in a new tab) |
Q3_K_M | 1.6 GB | 16.0 GB | 3,239 tok/s | Runs well | 44 ptsUsable (C) · 44 of 100 points: Model quality 2.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.3/15 | GGUF (opens in a new tab) |
Q4_K_M | 1.6 GB | 16.0 GB | 2,506 tok/s | Runs well | 44 ptsUsable (C) · 44 of 100 points: Model quality 2.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.3/15 | GGUF (opens in a new tab) |
Q5_K_M | 1.6 GB | 16.0 GB | 2,070 tok/s | Runs well | 44 ptsUsable (C) · 44 of 100 points: Model quality 2.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.3/15 | GGUF (opens in a new tab) |
Q6_K | 1.7 GB | 16.0 GB | 1,738 tok/s | Runs well | 45 ptsUsable (C) · 45 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.4/15 | GGUF (opens in a new tab) |
Q8_0 | 1.7 GB | 16.0 GB | 1,276 tok/s | Runs well | 45 ptsUsable (C) · 45 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.5/15 | GGUF (opens in a new tab) |
F16 | 2.0 GB | 16.0 GB | 588 tok/s | Runs well | 45 ptsUsable (C) · 45 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.8/15 | 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
- 47 ptsUsable (C) · 47 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.5/15 NVIDIA GeForce RTX 4060
- 47 ptsUsable (C) · 47 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15 Apple M1 (16 GB)
- 47 ptsUsable (C) · 47 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15 Apple M4 (16 GB)
- 46 ptsUsable (C) · 46 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.5/15 Apple M4 (24 GB)
- 46 ptsUsable (C) · 46 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.5/15 Apple M4 Pro (24 GB)
- 46 ptsUsable (C) · 46 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.4/15 Intel Arc B580
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.