Bernini-R A14B (video generation + editing) on Raspberry Pi 4 (8 GB)

Can the Raspberry Pi 4 (8 GB) run Bernini-R A14B (video generation + editing)?

No, it doesn’t fit

No. Bernini-R A14B (video generation + editing) needs about 10.7 GB even at FP8; the Raspberry Pi 4 (8 GB) has 6.4 GB usable.

Download Bernini-R A14B (video generation + editing)

Original weights · Hugging Face

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

huggingface.co/ByteDance/Bernini-R (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/bytedance/Bernini (opens in a new tab)

git clone https://github.com/bytedance/Bernini

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP8, 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 Raspberry Pi 4 (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.	11.2 / 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	11.2 / 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 Raspberry Pi 4 (8 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	15.7 GB	6.4 GB	—	—	Won't run	85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	10.7 GB	6.4 GB	—	—	Won't run	83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 9.0 min · Runs well
85Excellent quality (A+) · 85 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. NVIDIA RTX PRO 6000 Blackwell FP16 · 8.8 min · Runs well
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 11.3 min · Runs well (encoder in RAM)
85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 80GB SXM FP16 · 8.4 min · Runs well
85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H800 80GB SXM FP16 · 8.4 min · Runs well
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA A100 40GB FP8 · 11.3 min · Runs well

Models that do run on the Raspberry Pi 4 (8 GB)

35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5) FP16 · 82.2 min
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2B FP8 · 1.6 h
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B FP8 · 14.4 h
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B I2V FP8 · 14.4 h
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3B FP8 · 19.2 h

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.