Falcon-H1 Tiny 90M Instruct on LattePanda Sigma (32 GB)

Can the LattePanda Sigma (32 GB) run Falcon-H1 Tiny 90M Instruct?

Yes, it runs great

Runs great. At F16 it needs about 2.2 GB of 16.0 GB usable, with an estimated speed of 250 tok/s.

Best quant: F16
Generation speed: 250 tok/s
Prompt processing: 22,611 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 256K

Best app to run it on the LattePanda Sigma (32 GB)

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

Supports Intel Arc through SYCL and Vulkan.
Usually the first to support new model architectures.

llama-server -hf tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF:BF16

LM Studio Point-and-click: search, download and chat in one window.
Jan Point-and-click: search, download and chat in one window.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Supports Intel Arc through SYCL and Vulkan.
Usually the first to support new model architectures.

llama-server -hf tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF:BF16

LM Studio Point-and-click: search, download and chat in one window.
Jan Point-and-click: search, download and chat in one window.

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 Falcon-H1 Tiny 90M Instruct

Recommended for the LattePanda Sigma (32 GB): F16. 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 F16 file (opens in a new tab)

huggingface.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF (opens in a new tab)

ollama run hf.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/tiiuae/Falcon-H1-Tiny-90M-Instruct (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 3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at F16, 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 LattePanda Sigma (32 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.	1.4 / 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.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	3.1 / 15
Match score	44.5 / 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 (32 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	2.0 GB	16.0 GB	1,752 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	2.0 GB	16.0 GB	1,379 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	2.0 GB	16.0 GB	1,067 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	2.0 GB	16.0 GB	881 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	2.1 GB	16.0 GB	740 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	2.1 GB	16.0 GB	543 tok/s	Runs great	3Basic quality (D) · 3 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	2.2 GB	16.0 GB	250 tok/s	Runs great	3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (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

3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) F16 · 188 tok/s · Runs great
3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) F16 · 331 tok/s · Runs great
3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 870 tok/s · Runs great
3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (24 GB) F16 · 331 tok/s · Runs great
3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) F16 · 752 tok/s · Runs great
3Basic quality (D) · 3 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 1,115 tok/s · Runs great

Quick answers

How much memory does Falcon-H1 Tiny 90M Instruct need on the LattePanda Sigma 32GB?

At F16 it needs about 2.2 GB. The LattePanda Sigma 32GB has 16.0 GB usable.

How fast is Falcon-H1 Tiny 90M Instruct on the LattePanda Sigma 32GB?

About 250 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Falcon-H1 Tiny 90M Instruct should I use on the LattePanda Sigma 32GB?

F16. 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.