Can the LattePanda 3 Delta (8 GB) run Iris-3B?

No, it doesn’t fit

No. Iris-3B needs about 4.5 GB even at FP16; the LattePanda 3 Delta (8 GB) has 4.0 GB usable.

Download Iris-3B

Original weights · Hugging Face

Full-precision files from Speridlabs, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/speridlabs/iris-3b (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Speridlabs. The weights themselves download from Hugging Face.

github.com/speridlabs/iris-3b (opens in a new tab)

git clone https://github.com/speridlabs/iris-3b

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

How it’s rated

Quality 72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP16, 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 Won't run: How well it runs on the LattePanda 3 Delta (8 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.	9.7 / 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.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	9.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every precision on the LattePanda 3 Delta (8 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	4.5 GB	4.0 GB	—	—	Won't run	72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP16 · 16 s · Runs well
72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP16 · 18 s · Runs well
72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP16 · 13 s · Runs well
72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP16 · 30 s · Runs well
72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER FP16 · 32 s · Runs well (encoder in RAM)
72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 12 s · Runs great

Models that do run on the LattePanda 3 Delta (8 GB)

28Basic quality (D) · 28 of 100: how capable the model is at FP16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. SD Turbo FP16 · 5.7 s
84Great quality (A) · 84 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image Klein Q4 GGUF · 10.6 min
39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. SDXL Turbo FP8 · 10 s
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. LCM DreamShaper v7 FP16 · 18 s
76Great quality (A) · 76 of 100: how capable the model is at FP8, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4B FP8 · 84 s

Quick answers

How much memory does Iris-3B need on the LattePanda 3 Delta 8GB?

Even at FP16 it needs about 4.5 GB; the LattePanda 3 Delta 8GB has 4.0 GB usable.

What can the LattePanda 3 Delta 8GB run instead?

SD Turbo, FLUX 3 Image Klein and SDXL Turbo run best on it; the page lists more, each with its speed.

What hardware can run Iris-3B?

RTX 4090 24GB, RX 7900 XTX 24GB and RTX 5090 32GB run it; the page lists more, each with its speed.

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.