Deepseek Coder 6.7B Instruct — Hardware Requirements & GPU Compatibility
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DeepSeek Coder 6.7B Instruct is a first-generation code-specialized model trained on a large corpus of source code and programming-related data. At 6.7 billion parameters, it provides solid code completion, generation, and explanation capabilities across popular programming languages while remaining small enough to run on most consumer GPUs. While newer models in the DeepSeek lineup have surpassed it in raw capability, this model remains a practical choice for users who need a lightweight local coding assistant with minimal hardware requirements. It runs well on GPUs with as little as 6 GB of VRAM when quantized.
143.7K downloads
496 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 6.7B
- 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 6.7B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.2 GB | 11.8 GB | 2.86 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.3 GB | 11.8 GB | 2.95 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.7 GB | 12.2 GB | 3.29 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.7 GB | 12.3 GB | 3.37 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 12.9 GB | 4.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.2 GB | 13.7 GB | 4.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.9 GB | 14.4 GB | 5.56 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.1 GB | 15.6 GB | 6.74 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 6.7B Instruct?
Q4_K_M · 5.4 GBDeepseek Coder 6.7B Instruct (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 16K context window can add up to 7.5 GB, bringing total usage to 12.9 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Deepseek Coder 6.7B Instruct?
Q4_K_M · 5.4 GB58 devices with unified memory can run Deepseek Coder 6.7B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Deepseek Coder 6.7B 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 6.7B Instruct need?
Deepseek Coder 6.7B Instruct requires 5.4 GB of VRAM at Q4_K_M, or 14.8 GB at BF16. Full 16K context adds up to 7.5 GB (12.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 4.8 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 16K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context12.9 GB- What's the best quantization for Deepseek Coder 6.7B Instruct?
For Deepseek Coder 6.7B Instruct, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.6 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 4.2 GB.
VRAM requirement by quantization
IQ3_XS4.2 GBQ3_K_M4.7 GBQ4_15.2 GBQ4_K_M ★5.4 GBQ5_K_S6.0 GBBF1614.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Deepseek Coder 6.7B Instruct on a Mac?
Deepseek Coder 6.7B Instruct requires at least 4.2 GB at IQ3_XS, 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 6.7B Instruct locally?
Yes — Deepseek Coder 6.7B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Coder 6.7B Instruct?
At Q4_K_M, Deepseek Coder 6.7B Instruct can reach ~812 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~121 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 ÷ 5.4 × 0.65 = ~959 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~959 tok/s~121 tok/s~959 tok/s~812 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 6.7B Instruct?
At Q4_K_M, the download is about 4.04 GB. The full-precision BF16 version is 13.48 GB. The smallest option (IQ3_XS) is 2.78 GB.
- Which GPUs can run Deepseek Coder 6.7B Instruct?
50 consumer GPUs can run Deepseek Coder 6.7B Instruct at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Deepseek Coder 6.7B Instruct?
59 devices with unified memory can run Deepseek Coder 6.7B Instruct at Q4_K_M (5.4 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.