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Can the AMD Radeon RX 9060 XT 8GB run Llama 3.1 8B Instruct?

74 ptsGreat (A) · 74 of 100 points: Model quality 20.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 7.0 GB of 8.0 GB usable, with an estimated speed of 42.1 tok/s.

Best quant
Q4_K_M
Generation speed
42.1 tok/s
Prompt processing
3,953 tok/s
First token (1K prompt)
0.3 s
Largest safe context
15K

Download Llama 3.1 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 llama3.1:8b-instruct-q4_K_M

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 Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Llama-3.1-8B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/meta-llama/Llama-3.1-8B-Instruct (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Meta. The weights themselves download from Hugging Face.

github.com/meta-llama/llama-models (opens in a new tab)

git clone https://github.com/meta-llama/llama-models

Meta’s official Llama repo: model cards, prompt formats and download tools.

How the A 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.20.5 / 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
Total74.5 / 10074 ptsGreat (A) · 74 of 100 points: Model quality 20.5/45, 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 AMD Radeon RX 9060 XT 8GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.3 GB 8.0 GB 69.2 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 6.0 GB 8.0 GB 54.5 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 18.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 7.0 GB 8.0 GB 42.1 tok/s Tight fit 74 ptsGreat (A) · 74 of 100 points: Model quality 20.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.8 GB 8.0 GB 34.8 tok/s Tight fit 73 ptsGreat (A) · 73 of 100 points: Model quality 21.0/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 8.7 GB 8.0 GB 22.3 tok/s Runs with offload 57 ptsUsable (C) · 57 of 100 points: Model quality 21.4/45, Memory fit 10.0/25, Speed 11.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 10.6 GB 8.0 GB 11.0 tok/s Heavy offload 48 ptsUsable (C) · 48 of 100 points: Model quality 21.6/45, Memory fit 4.0/25, Speed 7.9/15, Memory use 15.0/15 GGUF (UD-Q8_K_XL) (opens in a new tab)
F16 18.1 GB 8.0 GB 3.3 tok/s Heavy offload 44 ptsUsable (C) · 44 of 100 points: Model quality 21.6/45, Memory fit 4.0/25, Speed 4.3/15, Memory use 15.0/15 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.