Ling 3.0 Tiny (8B-A1.3B) on AMD Ryzen 7 8845HS (Radeon 780M) (16 GB)

Can the AMD Ryzen 7 8845HS (Radeon 780M) (16 GB) run Ling 3.0 Tiny (8B-A1.3B)?

Yes, it runs well (tight fit)

Runs well (tight fit). At Q6_K it needs about 7.9 GB of 8.0 GB usable, with an estimated speed of 42.8 tok/s.

Best quant: Q6_K
Generation speed: 42.8 tok/s
Prompt processing: 7,917 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 10K

Best app to run it on the AMD Ryzen 7 8845HS (Radeon 780M) (16 GB)

On Windows

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.

lemonade pull inclusionAI/Ling-3.0-tiny-GGUF:Q6_K

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

On Linux

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.

lemonade pull inclusionAI/Ling-3.0-tiny-GGUF:Q6_K

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 Ling 3.0 Tiny (8B-A1.3B)

Recommended for the AMD Ryzen 7 8845HS (Radeon 780M) (16 GB): Q6_K. 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 Q6_K file (opens in a new tab)

huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF (opens in a new tab)

ollama run hf.co/inclusionAI/Ling-3.0-tiny-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/inclusionAI/Ling-3.0-tiny (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 59Good quality (B) · 59 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q6_K, 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 AMD Ryzen 7 8845HS (Radeon 780M) (16 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.	26.7 / 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.	24.0 / 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
Match score	80.7 / 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 AMD Ryzen 7 8845HS (Radeon 780M) (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	4.6 GB	8.0 GB	87.1 tok/s	Runs great	42Fair quality (C) · 42 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q3_K_M`	5.3 GB	8.0 GB	72.2 tok/s	Runs great	50Fair quality (C) · 50 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q4_K_M`	6.3 GB	8.0 GB	58.5 tok/s	Runs great	57Good quality (B) · 57 of 100: how capable the model is at Q4_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)
`Q5_K_M`	7.1 GB	8.0 GB	49.7 tok/s	Runs welltight fit	58Good quality (B) · 58 of 100: how capable the model is at Q5_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)
`Q6_K`	7.9 GB	8.0 GB	42.8 tok/s	Runs welltight fit	59Good quality (B) · 59 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	9.8 GB	8.0 GB	27.6 tok/s	Slowfar over GPU memory limit	60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	17.2 GB	8.0 GB	—	Won't run	60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking.	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

60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX F16 · 102 tok/s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (24 GB) Q8_0 · 41.8 tok/s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) Q8_0 · 79.0 tok/s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 Q8_0 · 102 tok/s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB Q8_0 · 104 tok/s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 F16 · 109 tok/s · Runs great

Quick answers

How much memory does Ling 3.0 Tiny (8B-A1.3B) need on the AMD Ryzen 7 8845HS (Radeon 780M) 16GB?

At Q6_K it needs about 7.9 GB. The AMD Ryzen 7 8845HS (Radeon 780M) 16GB has 8.0 GB usable.

How fast is Ling 3.0 Tiny (8B-A1.3B) on the AMD Ryzen 7 8845HS (Radeon 780M) 16GB?

About 42.8 tok/s at Q6_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Ling 3.0 Tiny (8B-A1.3B) should I use on the AMD Ryzen 7 8845HS (Radeon 780M) 16GB?

Q6_K. 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.