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Can the NVIDIA RTX PRO 6000 Blackwell run Yi 1.5 34B Chat?

70 ptsGood (B) · 70 of 100 points: Model quality 22.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15

Yes, it runs well

Runs well. At Q6_K it needs about 34.6 GB of 96.0 GB usable, with an estimated speed of 44.4 tok/s.

Best quant
Q6_K
Generation speed
44.4 tok/s
Prompt processing
2,617 tok/s
First token (1K prompt)
0.4 s
Largest safe context
4K

Download Yi 1.5 34B Chat

Recommended for the NVIDIA RTX PRO 6000 Blackwell: Q6_K. 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:34b-chat-v1.5-q6_K

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 Q6_K file (opens in a new tab)

huggingface.co/lmstudio-community/Yi-1.5-34B-Chat-GGUF (opens in a new tab)

ollama run hf.co/lmstudio-community/Yi-1.5-34B-Chat-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/01-ai/Yi-1.5-34B-Chat (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from 01.AI. The weights themselves download from Hugging Face.

github.com/01-ai/Yi-1.5 (opens in a new tab)

git clone https://github.com/01-ai/Yi-1.5

Links checked 7 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.22.3 / 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.8.3 / 15
Total70.6 / 10070 ptsGood (B) · 70 of 100 points: Model quality 22.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA RTX PRO 6000 Blackwell

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 20.2 GB 96.0 GB 105 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 15.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.8/15 GGUF (opens in a new tab)
Q3_K_M 23.3 GB 96.0 GB 82.7 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.6/15 GGUF (Q3_K_L) (opens in a new tab)
Q4_K_M 27.4 GB 96.0 GB 64.0 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15 GGUF (opens in a new tab)
Q5_K_M 30.8 GB 96.0 GB 52.8 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 21.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.4/15 GGUF (opens in a new tab)
Q6_K 34.6 GB 96.0 GB 44.4 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 22.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 GGUF (opens in a new tab)
Q8_0 42.9 GB 96.0 GB 32.6 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 22.5/45, Memory fit 25.0/25, Speed 13.5/15, Memory use 10.3/15 GGUF (opens in a new tab)
F16 75.2 GB 96.0 GB 15.0 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 22.5/45, Memory fit 25.0/25, Speed 9.2/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 4K 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.