Qwen1.5 110B Chat on NVIDIA RTX PRO 5000 Blackwell 72GB

Can the NVIDIA RTX PRO 5000 Blackwell 72GB run Qwen1.5 110B Chat?

Yes, it runs well (tight fit)

Runs well (tight fit). At Q3_K_M it needs about 61.4 GB of 72.0 GB usable, with an estimated speed of 19.2 tok/s.

Best quant: Q3_K_M
Generation speed: 19.2 tok/s
Prompt processing: 421 tok/s
First token (1K prompt): 2.4 s
Largest safe context: 32K

Best app to run it on the NVIDIA RTX PRO 5000 Blackwell 72GB

On Windows

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run qwen:110b-chat-v1.5-q3_K_M

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run qwen:110b-chat-v1.5-q3_K_M

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Qwen1.5 110B Chat

Recommended for the NVIDIA RTX PRO 5000 Blackwell 72GB: Q3_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.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run qwen:110b-chat-v1.5-q3_K_M

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/Qwen/Qwen1.5-110B-Chat-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen1.5-110B-Chat-GGUF

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen1.5-110B-Chat (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q3_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welltight fit: How well it runs on the NVIDIA RTX PRO 5000 Blackwell 72GB: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	19.7 / 45
Memory fitFull marks when everything sits in GPU 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.	10.4 / 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
Match score	69.0 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA RTX PRO 5000 Blackwell 72GB

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	51.4 GB	72.0 GB	24.4 tok/s	Runs well	36Basic quality (D) · 36 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	61.4 GB	72.0 GB	19.2 tok/s	Runs welltight fit	43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.
`Q4_K_M`	74.7 GB	72.0 GB	8.4 tok/s	Slowoffloads to RAM	49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.
`Q5_K_M`	85.8 GB	72.0 GB	2.8 tok/s	Very slowoffloads to RAM	50Fair quality (C) · 50 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.
`Q6_K`	98.1 GB	72.0 GB	—	Won't run	51Fair quality (C) · 51 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.
`Q8_0`	125 GB	72.0 GB	—	Won't run	52Fair quality (C) · 52 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.
`F16`	229 GB	72.0 GB	—	Won't run	52Fair quality (C) · 52 of 100: how capable the model is at F16, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	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

49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Q4_K_M · 19.8 tok/s · Runs well
43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (256 GB) Q3_K_M · 10.1 tok/s · Runs well
36Basic quality (D) · 36 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) Q2_K · 12.5 tok/s · Runs well
43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (512 GB) Q3_K_M · 10.1 tok/s · Runs well
52Fair quality (C) · 52 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200 Q8_0 · 45.0 tok/s · Runs great
50Fair quality (C) · 50 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 141GB SXM Q5_K_M · 43.8 tok/s · Runs great

Quick answers

How much memory does Qwen1.5 110B Chat need on the NVIDIA RTX PRO 5000 Blackwell 72GB?

At Q3_K_M it needs about 61.4 GB. The NVIDIA RTX PRO 5000 Blackwell 72GB has 72.0 GB usable.

How fast is Qwen1.5 110B Chat on the NVIDIA RTX PRO 5000 Blackwell 72GB?

About 19.2 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen1.5 110B Chat should I use on the NVIDIA RTX PRO 5000 Blackwell 72GB?

Q3_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.