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Can the Raspberry Pi 4 (4 GB) run DeepSeek Coder 1.3B Instruct?

8Basic quality (D) · 8 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 34/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (over memory limit)

Slow (over memory limit). At Q2_K it needs about 3.0 GB of 2.8 GB usable, with an estimated speed of 6.8 tok/s.

Best quant
Q2_K
Generation speed
6.8 tok/s
Prompt processing
22.2 tok/s
First token (1K prompt)
46.1 s
Largest safe context
7K

Best app to run it on the Raspberry Pi 4 (4 GB)

On Linux on ARM boards

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open DeepSeek Coder 1.3B Instruct in LM Studio

  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.

Want an agent? DeepSeek Coder 1.3B Instruct supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 DeepSeek Coder 1.3B Instruct

Recommended for the Raspberry Pi 4 (4 GB): Q2_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run deepseek-coder:1.3b-instruct-q2_K

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/mradermacher/deepseek-coder-1.3b-instruct-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/deepseek-coder-1.3b-instruct-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct (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 8Basic quality (D) · 8 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 34/100 (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 Slowover memory limit
How well it runs on the Raspberry Pi 4 (4 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.3.8 / 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.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.6.2 / 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 score35.0 / 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 Raspberry Pi 4 (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 3.0 GB 2.8 GB 6.8 tok/s Slowover memory limit 8Basic quality (D) · 8 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 34/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 3.1 GB 2.8 GB 5.3 tok/s Slowfar over memory limit 10Basic quality (D) · 10 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 29/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 3.3 GB 2.8 GB 4.1 tok/s Slowfar over memory limit 11Basic quality (D) · 11 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 28/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 3.4 GB 2.8 GB 3.4 tok/s Slowfar over memory limit 11Basic quality (D) · 11 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 28/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 3.5 GB 2.8 GB — Won't run 11Basic quality (D) · 11 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 1/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 3.9 GB 2.8 GB — Won't run 12Basic quality (D) · 12 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 1/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 5.1 GB 2.8 GB — Won't run 12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 1/100 (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.

Other hardware that runs it

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.

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