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Can the NVIDIA GeForce RTX 5080 run Yi Coder 9B Chat?

73 ptsGreat (A) · 73 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 11.6 GB of 16.0 GB usable, with an estimated speed of 67.9 tok/s.

Best quant
Q8_0
Generation speed
67.9 tok/s
Prompt processing
4,591 tok/s
First token (1K prompt)
0.3 s
Largest safe context
52K

Download Yi Coder 9B Chat

Recommended for the NVIDIA GeForce RTX 5080: Q8_0. 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 yi-coder:9b-chat-q8_0

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 Q8_0 file (opens in a new tab)

huggingface.co/bartowski/Yi-Coder-9B-Chat-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Yi-Coder-9B-Chat-GGUF:Q8_0

Original weights · Hugging Face

Full-precision files from 01.AI, for fine-tuning, vLLM, Transformers.

huggingface.co/01-ai/Yi-Coder-9B-Chat (opens in a new tab)

No sign-in needed.

Links checked 1 week ago against Hugging Face, Ollama and GitHub.

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.18.0 / 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.15.0 / 15
Total73.0 / 10073 ptsGreat (A) · 73 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, 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 5080

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.7 GB 16.0 GB 219 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 12.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 GGUF (opens in a new tab)
Q3_K_M 6.5 GB 16.0 GB 173 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.4/15 GGUF (opens in a new tab)
Q4_K_M 7.6 GB 16.0 GB 133 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 GGUF (opens in a new tab)
Q5_K_M 8.5 GB 16.0 GB 110 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 17.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.2/15 GGUF (opens in a new tab)
Q6_K 9.4 GB 16.0 GB 92.6 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 17.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.6/15 GGUF (opens in a new tab)
Q8_0 11.6 GB 16.0 GB 67.9 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 19.9 GB 16.0 GB 8.0 tok/s Runs with offload 49 ptsUsable (C) · 49 of 100 points: Model quality 18.0/45, Memory fit 10.0/25, Speed 6.7/15, 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.