Ling Flash 2.0 (100B-A6B) on NVIDIA GB10 (128 GB)

Can the NVIDIA GB10 (128 GB) run Ling Flash 2.0 (100B-A6B)?

Yes, it runs great

Runs great. At Q5_K_M it needs about 83.4 GB of 115 GB usable, with an estimated speed of 39.2 tok/s.

Best quant: Q5_K_M
Generation speed: 39.2 tok/s
Prompt processing: 3,659 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 32K

Best app to run it on the NVIDIA GB10 (128 GB)

On Linux

vLLM

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

vllm serve inclusionAI/Ling-flash-2.0

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

Want an agent? Ling Flash 2.0 (100B-A6B) supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 Ling Flash 2.0 (100B-A6B)

Recommended for the NVIDIA GB10 (128 GB): Q5_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q5_K_M files (opens in a new tab)

huggingface.co/bartowski/inclusionAI_Ling-flash-2.0-GGUF (opens in a new tab)

ollama run hf.co/bartowski/inclusionAI_Ling-flash-2.0-GGUF

Original weights · Hugging Face

Full-precision files from Ant Group (inclusionAI), for fine-tuning, vLLM, Transformers.

huggingface.co/inclusionAI/Ling-flash-2.0 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ant Group (inclusionAI). The weights themselves download from Hugging Face.

github.com/inclusionAI/Ling-V2 (opens in a new tab)

git clone https://github.com/inclusionAI/Ling-V2

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 73Great quality (A) · 73 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q5_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 great: How well it runs on the NVIDIA GB10 (128 GB): 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.	33.2 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	14.8 / 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	88.0 / 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 GB10 (128 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	51.5 GB	115 GB	70.4 tok/s	Runs great	53Fair quality (C) · 53 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	60.8 GB	115 GB	57.7 tok/s	Runs great	63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	73.1 GB	115 GB	46.4 tok/s	Runs great	72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`Q5_K_M`	83.4 GB	115 GB	39.2 tok/s	Runs great	73Great quality (A) · 73 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`Q6_K`	94.7 GB	115 GB	33.5 tok/s	Runs great	75Great quality (A) · 75 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`Q8_0`	119 GB	115 GB	—	Won't run	76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`F16`	216 GB	115 GB	—	Won't run	76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (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

76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) Q8_0 · 55.6 tok/s · Runs great
76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (256 GB) Q8_0 · 56.7 tok/s · Runs great
73Great quality (A) · 73 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Q5_K_M · 146 tok/s · Runs great
73Great quality (A) · 73 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX Spark N1X (6144-core GPU) (128 GB) Q5_K_M · 42.5 tok/s · Runs great
75Great quality (A) · 75 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (128 GB) Q6_K · 53.1 tok/s · Runs well (tight fit)
72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) Q4_K_M · 38.6 tok/s · Runs great

Quick answers

How much memory does Ling Flash 2.0 (100B-A6B) need on the NVIDIA GB10 128GB?

At Q5_K_M it needs about 83.4 GB. The NVIDIA GB10 128GB has 115 GB usable.

How fast is Ling Flash 2.0 (100B-A6B) on the NVIDIA GB10 128GB?

About 39.2 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Ling Flash 2.0 (100B-A6B) should I use on the NVIDIA GB10 128GB?

Q5_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.