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.
43.3K downloads
167 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 1.3B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,256
- Release Date
- 2023-10-29
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.3 GB | 4.1 GB | 0.57 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.3 GB | 4.1 GB | 0.59 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.4 GB | 4.2 GB | 0.66 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.4 GB | 4.2 GB | 0.67 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.5 GB | 4.3 GB | 0.81 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.7 GB | 4.5 GB | 0.96 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.8 GB | 4.6 GB | 1.11 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.0 GB | 4.9 GB | 1.35 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Deepseek Coder 1.3B Instruct?
Q4_K_M · 1.5 GBDeepseek Coder 1.3B Instruct (Q4_K_M) requires 1.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 16K context window can add up to 2.8 GB, bringing total usage to 4.3 GB. 50 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?
Q4_K_M · 1.5 GB59 devices with unified memory can run Deepseek Coder 1.3B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Deepseek Coder 1.3B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Deepseek Coder 1.3B Instruct need?
Deepseek Coder 1.3B Instruct requires 1.5 GB of VRAM at Q4_K_M, or 3.4 GB at BF16. Full 16K context adds up to 2.8 GB (4.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 4.8 bits ÷ 8 = 0.8 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
Q4_K_M1.5 GBQ4_K_M + full context4.3 GB- What's the best quantization for Deepseek Coder 1.3B Instruct?
For Deepseek Coder 1.3B Instruct, Q4_K_M (1.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.3 GB.
VRAM requirement by quantization
Q2_K1.3 GBQ4_01.4 GBQ4_K_M ★1.5 GBQ5_01.5 GBQ5_K_M1.7 GBBF163.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Deepseek Coder 1.3B Instruct on a Mac?
Deepseek Coder 1.3B Instruct requires at least 1.3 GB at Q2_K, 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 Q4_K_M quantization it needs 1.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Coder 1.3B Instruct?
At Q4_K_M, Deepseek Coder 1.3B Instruct can reach ~2914 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~434 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.5 × 0.65 = ~3444 tok/s
Estimated speed at Q4_K_M (1.5 GB)
~3444 tok/s~434 tok/s~3444 tok/s~2914 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 Q4_K_M, the download is about 0.81 GB. The full-precision BF16 version is 2.69 GB. The smallest option (Q2_K) is 0.57 GB.
- Which GPUs can run Deepseek Coder 1.3B Instruct?
50 consumer GPUs can run Deepseek Coder 1.3B Instruct at Q4_K_M (1.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Deepseek Coder 1.3B Instruct?
59 devices with unified memory can run Deepseek Coder 1.3B Instruct at Q4_K_M (1.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.