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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~121 tok/s · 16K)Macs from 16 GB (Q4_K_M · ~26 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Deepseek Coder 6.7B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run deepseek-coder:6.7b-instruct-q4_K_MQ4_K_M fits fully on the RTX 4090 · 11.1 GB free · 16K context
Download: deepseek-coder-6.7b-instruct.Q4_K_M.gguf4.08 GBTheBloke
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 5070 Ti (16 GB) (3 GB headroom) · Q4_K_M
- Generation speed
- ~108 tok/s
- generation speed
- Cost per 1M output tokens
- $0.42
- per 1M output tokens
How Much VRAM Does Deepseek Coder 6.7B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 16K context · ~155 tok/s | |
| 4.3 GB | — | 3-bit small — 3-bit small quantization | Fits: 16K context · ~152 tok/s | |
| 4.7 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~141 tok/s | |
| 4.7 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 16K context · ~138 tok/s | |
| 5.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~121 tok/s | |
| 6.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~106 tok/s | |
| 6.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 16K context · ~95 tok/s | |
| 8.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 16K context · ~81 tok/s |
Selected Q4_K_M
4-bit medium quantization12.9GBat 16K context
- Weights
- 4.1 GB
- KV cache
- 8.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 11.1 GB free · ~121 tok/s
≥50 tok/s needs ≥417 GB/s · RTX 5090 ~215, RTX 3090 Ti ~121
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Deepseek Coder 6.7B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
16–18 GB
16–18 GB tier →fits · 3.1 GB free · up to 16K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~115Simulate output on RTX 5080RTX 5070 Ti~108Simulate output on RTX 5070 TiRTX 4080 SUPER~88Simulate output on RTX 4080 SUPERM516 GB~20Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~19Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~20Simulate output on iPad Pro M5 13" (16 GB)Orin NX~12Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 7.1 GB free · up to 16K ctx
1 GPU
RX 7900 XT~89Simulate output on RX 7900 XT32–40 GB
fits · 19.1 GB free · up to 16K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~215Simulate output on RTX 5090M5 Max36 GB~59Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Deepseek Coder 6.7B Instruct
DeepSeek Coder family · 11 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
| Chatglm3 6BZ.ai | — | 5.2 GB | ~159 | same size |
Fine-tunes & variants(1)
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 8.9 GB (at full 16K context)
VRAM usage by quantization
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 IQ2_XXS at 3.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Deepseek Coder 6.7B Instruct on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Deepseek Coder 6.7B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Deepseek Coder 6.7B Instruct locally?
Yes — Deepseek Coder 6.7B Instruct can run locally on consumer hardware. 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 ~886 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 ÷ 5.42 × 0.65 = ~959 tok/s
Estimated speed at Q4_K_M (5.4 GB)
Real-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 (IQ2_XXS) is 1.85 GB.
Which GPUs can run Deepseek Coder 6.7B Instruct?
52 consumer GPUs can run Deepseek Coder 6.7B Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Deepseek Coder 6.7B Instruct?
151 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.