Groot 19B A3B 261023 on LattePanda Sigma (16 GB)

Can the LattePanda Sigma (16 GB) run Groot 19B A3B 261023?

Yes, but slowly (far over GPU memory limit)

Slow (far over GPU memory limit). At Q2_K it needs about 9.2 GB of 8.0 GB usable, with an estimated speed of 38.4 tok/s.

Best quant: Q2_K
Generation speed: 38.4 tok/s
Prompt processing: 678 tok/s
First token (1K prompt): 1.5 s
Largest safe context: —

Best app to run it on the LattePanda Sigma (16 GB)

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

Splits the model between GPU memory and system RAM, which this setup needs.
Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
Supports Intel Arc through SYCL and Vulkan.

llama-server -hf bartowski/GrootTeam_Groot-19B-A3B-261023-GGUF:Q2_K

LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
Jan Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Splits the model between GPU memory and system RAM, which this setup needs.
Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
Supports Intel Arc through SYCL and Vulkan.

llama-server -hf bartowski/GrootTeam_Groot-19B-A3B-261023-GGUF:Q2_K

LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
Jan Splits the model between GPU memory and system RAM, which this setup needs.

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 Groot 19B A3B 261023

Recommended for the LattePanda Sigma (16 GB): Q2_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 Q2_K file (opens in a new tab)

huggingface.co/bartowski/GrootTeam_Groot-19B-A3B-261023-GGUF (opens in a new tab)

ollama run hf.co/bartowski/GrootTeam_Groot-19B-A3B-261023-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/GrootTeam/Groot-19B-A3B-261023 (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 39Basic quality (D) · 39 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_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 Slowfar over GPU memory limit: How well it runs on the LattePanda Sigma (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.	18.0 / 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.	4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	14.7 / 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	51.6 / 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 LattePanda Sigma (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	9.2 GB	8.0 GB	38.4 tok/s	Slowfar over GPU memory limit	39Basic quality (D) · 39 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	10.9 GB	8.0 GB	—	Won't run	47Fair quality (C) · 47 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	13.3 GB	8.0 GB	—	Won't run	54Fair quality (C) · 54 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	15.2 GB	8.0 GB	—	Won't run	55Good quality (B) · 55 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	17.3 GB	8.0 GB	—	Won't run	56Good quality (B) · 56 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	22.0 GB	8.0 GB	—	Won't run	57Good quality (B) · 57 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.	GGUF (opens in a new tab)
`F16`	40.3 GB	8.0 GB	—	Won't run	57Good quality (B) · 57 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.	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

57Good quality (B) · 57 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (48 GB) Q8_0 · 41.3 tok/s · Runs great
57Good quality (B) · 57 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q8_0 · 154 tok/s · Runs great
56Good quality (B) · 56 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. AMD Radeon RX 7900 XTX Q6_K · 127 tok/s · Runs great
56Good quality (B) · 56 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. NVIDIA GeForce RTX 3090 Q6_K · 136 tok/s · Runs great
56Good quality (B) · 56 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. NVIDIA GeForce RTX 4090 Q6_K · 141 tok/s · Runs great
54Fair quality (C) · 54 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q4_K_M · 128 tok/s · Runs great

Quick answers

How much memory does Groot 19B A3B 261023 need on the LattePanda Sigma 16GB?

At Q2_K it needs about 9.2 GB. The LattePanda Sigma 16GB has 8.0 GB usable.

How fast is Groot 19B A3B 261023 on the LattePanda Sigma 16GB?

About 38.4 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Groot 19B A3B 261023 should I use on the LattePanda Sigma 16GB?

Q2_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.