DeepSeek V4 Pro (1.6T-A49B) on Raspberry Pi 4 (2 GB)

Can the Raspberry Pi 4 (2 GB) run DeepSeek V4 Pro (1.6T-A49B)?

No, it doesn’t fit

No. DeepSeek V4 Pro (1.6T-A49B) needs about 661 GB even at Q2_K; the Raspberry Pi 4 (2 GB) has 1.2 GB usable.

Download DeepSeek V4 Pro (1.6T-A49B)

Ollama

Ollama offers this only as a cloud model (it runs on Ollama’s servers, not your machine). To run it locally, use the GGUF files below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/unsloth/DeepSeek-V4-Pro-0813-GGUF (opens in a new tab)

ollama run hf.co/unsloth/DeepSeek-V4-Pro-0813-GGUF

Original weights · Hugging Face

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

huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (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 Won't run: How well it runs on the Raspberry Pi 4 (2 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.	9.1 / 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	9.1 / 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 quantization on the Raspberry Pi 4 (2 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	661 GB	1.2 GB	—	Won't run	67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q3_K_M`	810 GB	1.2 GB	—	Won't run	80Great quality (A) · 80 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q4_K_M`	1,008 GB	1.2 GB	—	Won't run	91Excellent quality (A+) · 91 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q4_K_XL) · folder (opens in a new tab)
`Q5_K_M`	1,173 GB	1.2 GB	—	Won't run	93Excellent quality (A+) · 93 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q6_K`	1,355 GB	1.2 GB	—	Won't run	95Excellent quality (A+) · 95 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q8_0`	1,751 GB	1.2 GB	—	Won't run	96Excellent quality (A+) · 96 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q8_K_XL) · folder (opens in a new tab)
`F16`	3,302 GB	1.2 GB	—	Won't run	96Excellent quality (A+) · 96 of 100: how capable the model is at F16, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

None. No single GPU or device in our catalog runs this model on its own; it needs several GPUs working together.

Models that do run on the Raspberry Pi 4 (2 GB)

8Basic quality (D) · 8 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. LFM2.5 230M Q3_K_M · 36.8 tok/s
9Basic quality (D) · 9 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. LFM2.5 350M Q2_K · 30.7 tok/s
8Basic quality (D) · 8 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. Granite 4.0 H 350M Q2_K · 31.6 tok/s
8Basic quality (D) · 8 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. LFM2 350M Q2_K · 30.7 tok/s
4Basic quality (D) · 4 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. SmolVLM 256M Instruct Q2_K · 41.4 tok/s

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.