DeepSeek Coder v2 Lite Instruct — Hardware Requirements & GPU Compatibility
ChatCode
DeepSeek Coder V2 Lite Instruct is a code-focused mixture-of-experts model with 15.7 billion total parameters, trained to handle both programming tasks and general conversation. It supports a wide range of programming languages and excels at code generation, debugging, explanation, and refactoring. The MoE architecture keeps compute costs manageable despite the model's broad capabilities, and the Lite variant is sized to run on a single consumer GPU. For developers looking for a capable local coding assistant that can also handle general chat, this model offers an appealing combination of code specialization and practical hardware requirements.
894.1K downloads
609 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 15.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-06-14
- License
- Other
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HuggingFace
How Much VRAM Does DeepSeek Coder v2 Lite Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 7.4 GB | 43.2 GB | 6.68 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.6 GB | 43.4 GB | 6.87 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 8.4 GB | 44.2 GB | 7.66 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.6 GB | 44.4 GB | 7.85 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 10.2 GB | 46.0 GB | 9.42 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.9 GB | 47.7 GB | 11.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 13.7 GB | 49.5 GB | 12.96 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 16.5 GB | 52.3 GB | 15.71 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 v2 Lite Instruct?
Q4_K_M · 10.2 GBDeepSeek Coder v2 Lite Instruct (Q4_K_M) requires 10.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 164K context window can add up to 35.8 GB, bringing total usage to 46.0 GB. 37 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run DeepSeek Coder v2 Lite Instruct?
Q4_K_M · 10.2 GB48 devices with unified memory can run DeepSeek Coder v2 Lite Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin NX 16GB.
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download DeepSeek Coder v2 Lite Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek Coder v2 Lite Instruct need?
DeepSeek Coder v2 Lite Instruct requires 10.2 GB of VRAM at Q4_K_M, or 32.2 GB at BF16. Full 164K context adds up to 35.8 GB (46.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 15.7B × 4.8 bits ÷ 8 = 9.4 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 36.6 GB (at full 164K context)
VRAM usage by quantization
Q4_K_M10.2 GBQ4_K_M + full context46.0 GB- Can NVIDIA GeForce RTX 4090 run DeepSeek Coder v2 Lite Instruct?
Yes, at Q8_0 (16.5 GB) or lower. Higher quantizations like BF16 (32.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for DeepSeek Coder v2 Lite Instruct?
For DeepSeek Coder v2 Lite Instruct, Q4_K_M (10.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (10.6 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 7.2 GB.
VRAM requirement by quantization
IQ3_XS7.2 GBIQ3_M7.8 GBQ4_19.6 GBQ4_K_M ★10.2 GBQ5_K_S11.6 GBBF1632.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek Coder v2 Lite Instruct on a Mac?
DeepSeek Coder v2 Lite Instruct requires at least 7.2 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 v2 Lite Instruct locally?
Yes — DeepSeek Coder v2 Lite Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 10.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek Coder v2 Lite Instruct?
At Q4_K_M, DeepSeek Coder v2 Lite Instruct can reach ~432 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~64 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 ÷ 10.2 × 0.65 = ~511 tok/s
Estimated speed at Q4_K_M (10.2 GB)
~511 tok/s~64 tok/s~511 tok/s~432 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 v2 Lite Instruct?
At Q4_K_M, the download is about 9.42 GB. The full-precision BF16 version is 31.41 GB. The smallest option (IQ3_XS) is 6.48 GB.
- Which GPUs can run DeepSeek Coder v2 Lite Instruct?
37 consumer GPUs can run DeepSeek Coder v2 Lite Instruct at Q4_K_M (10.2 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run DeepSeek Coder v2 Lite Instruct?
52 devices with unified memory can run DeepSeek Coder v2 Lite Instruct at Q4_K_M (10.2 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.