DeepSeek Coder v2 Lite Instruct AWQ — Hardware Requirements & GPU Compatibility
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DeepSeek Coder v2 Lite Instruct AWQ is a 15.7B-parameter open language model from TechxGenus in the DeepSeek Coder family. It supports a context window of up to 163,840 tokens. At Q4_K_M it needs about 10.18 GB of VRAM — see which GPUs and Macs can run it below.
9.2K downloads
10 likesSpecifications
- Publisher
- TechxGenus
- Family
- DeepSeek Coder
- Parameters
- 15.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-06-22
- License
- Other
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How Much VRAM Does DeepSeek Coder v2 Lite Instruct AWQ 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 |
Which GPUs Can Run DeepSeek Coder v2 Lite Instruct AWQ?
Q4_K_M · 10.2 GBDeepSeek Coder v2 Lite Instruct AWQ (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. 27 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 AWQ?
Q4_K_M · 10.2 GB27 devices with unified memory can run DeepSeek Coder v2 Lite Instruct AWQ, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does DeepSeek Coder v2 Lite Instruct AWQ need?
DeepSeek Coder v2 Lite Instruct AWQ requires 10.2 GB of VRAM at Q4_K_M, or 16.5 GB at Q8_0. 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- What's the best quantization for DeepSeek Coder v2 Lite Instruct AWQ?
For DeepSeek Coder v2 Lite Instruct AWQ, 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 IQ2_XXS at 5.1 GB.
VRAM requirement by quantization
IQ2_XXS5.1 GBIQ3_XS7.2 GBQ4_08.6 GBQ4_19.6 GBQ4_K_M ★10.2 GBQ8_016.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek Coder v2 Lite Instruct AWQ on a Mac?
DeepSeek Coder v2 Lite Instruct AWQ requires at least 5.1 GB at IQ2_XXS, 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 AWQ locally?
Yes — DeepSeek Coder v2 Lite Instruct AWQ 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 AWQ?
At Q4_K_M, DeepSeek Coder v2 Lite Instruct AWQ can reach ~286 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 10.2 × 0.55 = ~286 tok/s
Estimated speed at Q4_K_M (10.2 GB)
~286 tok/s~64 tok/s~214 tok/s~177 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 AWQ?
At Q4_K_M, the download is about 9.42 GB. The full-precision Q8_0 version is 15.71 GB. The smallest option (IQ2_XXS) is 4.32 GB.
- Which GPUs can run DeepSeek Coder v2 Lite Instruct AWQ?
27 consumer GPUs can run DeepSeek Coder v2 Lite Instruct AWQ 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. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run DeepSeek Coder v2 Lite Instruct AWQ?
27 devices with unified memory can run DeepSeek Coder v2 Lite Instruct AWQ at Q4_K_M (10.2 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.