Deepseek Ai Deepseek Coder 6.7B Instruct GGUF — Hardware Requirements & GPU Compatibility
Code
110 downloadsSpecifications
- Publisher
- RichardErkhov
- Family
- DeepSeek Coder
- Parameters
- 6.7B
Get Started
How Much VRAM Does Deepseek Ai Deepseek Coder 6.7B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 14.7 GB | — | 13.40 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF?
BF16 · 14.7 GBDeepseek Ai Deepseek Coder 6.7B Instruct GGUF (BF16) requires 14.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF?
BF16 · 14.7 GB27 devices with unified memory can run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Deepseek Ai Deepseek Coder 6.7B Instruct GGUF need?
Deepseek Ai Deepseek Coder 6.7B Instruct GGUF requires 14.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 16 bits ÷ 8 = 13.4 GB
KV Cache + Overhead ≈ 1.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1614.7 GB- Can I run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF on a Mac?
Deepseek Ai Deepseek Coder 6.7B Instruct GGUF requires at least 14.7 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 Ai Deepseek Coder 6.7B Instruct GGUF locally?
Yes — Deepseek Ai Deepseek Coder 6.7B Instruct GGUF can run locally on consumer hardware. At BF16 quantization it needs 14.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Ai Deepseek Coder 6.7B Instruct GGUF?
At BF16, Deepseek Ai Deepseek Coder 6.7B Instruct GGUF can reach ~198 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~45 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 ÷ 14.7 × 0.55 = ~198 tok/s
Estimated speed at BF16 (14.7 GB)
AMD Instinct MI300X~198 tok/sNVIDIA GeForce RTX 4090~45 tok/sNVIDIA H100 SXM~148 tok/sAMD Instinct MI250X~122 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Deepseek Ai Deepseek Coder 6.7B Instruct GGUF?
At BF16, the download is about 13.40 GB.
- Which GPUs can run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF?
17 consumer GPUs can run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF at BF16 (14.7 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF?
27 devices with unified memory can run Deepseek Ai Deepseek Coder 6.7B Instruct GGUF at BF16 (14.7 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.