Deepseek Coder 1.3B Instruct — Hardware Requirements & GPU Compatibility
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DeepSeek Coder 1.3B Instruct is an ultra-compact code model designed for environments where hardware resources are extremely limited. Despite having just 1.3 billion parameters, it can handle basic code completion, simple generation tasks, and code Q&A across common programming languages. This is one of the smallest viable code models available, capable of running on integrated graphics or very low-end dedicated GPUs. It is well suited for edge deployment, embedded development environments, or as a fast local autocomplete engine where response speed matters more than handling complex multi-file reasoning tasks.
89.7K downloads
159 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 1.3B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,256
- Release Date
- 2024-03-07
- License
- Other
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HuggingFace
How Much VRAM Does Deepseek Coder 1.3B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 3.3 GB | 6.1 GB | 2.60 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Deepseek Coder 1.3B Instruct?
BF16 · 3.3 GBDeepseek Coder 1.3B Instruct (BF16) requires 3.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 16K context window can add up to 2.8 GB, bringing total usage to 6.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Deepseek Coder 1.3B Instruct?
BF16 · 3.3 GB33 devices with unified memory can run Deepseek Coder 1.3B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Deepseek Coder 1.3B Instruct need?
Deepseek Coder 1.3B Instruct requires 3.3 GB of VRAM at BF16. Full 16K context adds up to 2.8 GB (6.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 16 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.5 GB (at full 16K context)
VRAM usage by quantization
BF163.3 GBBF16 + full context6.1 GB- Can I run Deepseek Coder 1.3B Instruct on a Mac?
Deepseek Coder 1.3B Instruct requires at least 3.3 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Deepseek Coder 1.3B Instruct locally?
Yes — Deepseek Coder 1.3B Instruct can run locally on consumer hardware. At BF16 quantization it needs 3.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Coder 1.3B Instruct?
At BF16, Deepseek Coder 1.3B Instruct can reach ~883 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~199 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 3.3 × 0.55 = ~883 tok/s
Estimated speed at BF16 (3.3 GB)
~883 tok/s~199 tok/s~660 tok/s~546 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Deepseek Coder 1.3B Instruct?
At BF16, the download is about 2.60 GB.
- Which GPUs can run Deepseek Coder 1.3B Instruct?
35 consumer GPUs can run Deepseek Coder 1.3B Instruct at BF16 (3.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Deepseek Coder 1.3B Instruct?
33 devices with unified memory can run Deepseek Coder 1.3B Instruct at BF16 (3.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.