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Can the NVIDIA A800 40GB Active run Ling 2.6 Flash (104B-A7.4B)?

67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q3_K_M it needs about 55.3 GB of 40.0 GB usable, with an estimated speed of 38.9 tok/s.

Best quant
Q3_K_M
Generation speed
38.9 tok/s
Prompt processing
1,511 tok/s
First token (1K prompt)
0.7 s
Largest safe context
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Best app to run it on the NVIDIA A800 40GB Active

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Handy if you already work in Docker.
docker model run hf.co/inclusionAI/Ling-2.6-flash

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Handy if you already work in Docker.
docker model run hf.co/inclusionAI/Ling-2.6-flash

Want an agent? Ling 2.6 Flash (104B-A7.4B) 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 2.6 Flash (104B-A7.4B)

Original weights · Hugging Face

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

huggingface.co/inclusionAI/Ling-2.6-flash (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (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 welloffloads to RAM
How well it runs on the NVIDIA A800 40GB Active: 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.30.2 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.10.0 / 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 score70.0 / 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 A800 40GB Active

Quant Memory needed Available Speed Runs Quality Download
Q2_K 45.6 GB 40.0 GB 80.7 tok/s Runs welloffloads to RAM 56Good quality (B) · 56 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 55.3 GB 40.0 GB 38.9 tok/s Runs welloffloads to RAM 67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 68.2 GB 40.0 GB — Won't run 76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 79.0 GB 40.0 GB — Won't run 77Great quality (A) · 77 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 90.8 GB 40.0 GB — Won't run 79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 117 GB 40.0 GB — Won't run 80Great quality (A) · 80 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. All files (opens in a new tab)
F16 218 GB 40.0 GB — Won't run 80Great quality (A) · 80 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

Quick answers

How much memory does Ling 2.6 Flash (104B-A7.4B) need on the NVIDIA A800 40GB Active?

At Q3_K_M it needs about 55.3 GB. The NVIDIA A800 40GB Active has 40.0 GB usable.

How fast is Ling 2.6 Flash (104B-A7.4B) on the NVIDIA A800 40GB Active?

About 38.9 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Ling 2.6 Flash (104B-A7.4B) should I use on the NVIDIA A800 40GB Active?

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.

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