DeepSeek Coder v2 Lite Base — Hardware Requirements & GPU Compatibility
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DeepSeek Coder v2 Lite Base is a 15.7B-parameter open language model from DeepSeek 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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q2_K · 32K)Best on 24 GB (Q4_K_M · ~243 tok/s · 160K)Macs from 16 GB (Q4_K_M · ~56 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run DeepSeek Coder v2 Lite Base
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
vllm serve deepseek-ai/DeepSeek-Coder-V2-Lite-Base --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX A4000 (16 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~135 tok/s
- generation speed
- Cost per 1M output tokens
- $0.18
- per 1M output tokens
How Much VRAM Does DeepSeek Coder v2 Lite Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.4 GB | 8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 160K context · ~294 tok/s | |
| 8.4 GB | 9 GB | 3-bit medium — 3-bit medium quantization | Fits: 160K context · ~273 tok/s | |
| 10.2 GB | 10.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 160K context · ~243 tok/s | |
| 11.9 GB | 12.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 160K context · ~219 tok/s | |
| 13.7 GB | 14.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 160K context · ~199 tok/s | |
| 16.5 GB | 17 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 160K context · ~174 tok/s | |
| 32.2 GB | 32.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 32.2 GB |
Selected Q4_K_M
4-bit medium quantization10.2GBat 16K context
- Weights
- 9.4 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.8 GB free · ~243 tok/s
≥50 tok/s needs ≥143 GB/s · RTX 5090 ~337, RTX 3090 Ti ~243
Which Hardware Can Run DeepSeek Coder v2 Lite Base?
Q4_K_M · 10.2 GBDeepSeek Coder v2 Lite Base (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 4.6 GB, bringing total usage to 14.8 GB. 38 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run DeepSeek Coder v2 Lite Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 1.8 GB free · up to 64K ctx
10 GPUs
RTX 3080 Ti~228Simulate output on RTX 3080 TiRTX 5070~184Simulate output on RTX 5070RTX 4070~148Simulate output on RTX 407020 GB
fits · 9.8 GB free · up to 160K ctx
1 GPU
RX 7900 XT~111Simulate output on RX 7900 XT32–40 GB
fits · 21.8 GB free · up to 160K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~337Simulate output on RTX 5090M5 Max36 GB~93Simulate output on Mac Studio M5 Max (36 GB)M532 GB~47Simulate output on MacBook Air 13" M5 (32 GB)Orin~69Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~45Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is DeepSeek Coder v2 Lite Base?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 5 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent34.4%
#112/146 open
- MMLU
Epoch AI · Jun 2024Lab-reported
Lab-reported60.5%
#46/85 open
- GSM8K
Epoch AI · Jun 2024Lab-reported
Lab-reported67.1%
#21/62 open
- ARC Challenge
Epoch AI · Jun 2024Lab-reported
Lab-reported57.3%
#26/56 open
- WinoGrande
Epoch AI · Jun 2024Lab-reported
Lab-reported72.9%
#21/51 open
Alternatives to DeepSeek Coder v2 Lite Base
DeepSeek Coder family · 11 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Kimi VL A3B InstructMoonshot AI | — | 10.7 GB | ~208 | same size |
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| JiRackUltra 14BCMSManhattan | — | 12.4 GB | ~69 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
| LLaDA2.1 MiniInclusion AI | — | 33.5 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 15.7B
- Active parameters
- 2.7B per token
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-06-14
- License
- Other
Frequently Asked Questions
How much VRAM does DeepSeek Coder v2 Lite Base need?
DeepSeek Coder v2 Lite Base requires 10.2 GB of VRAM at Q4_K_M, or 32.2 GB at BF16. Full 164K context adds up to 4.6 GB (14.8 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)
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 ≈ 5.4 GB (at full 164K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run DeepSeek Coder v2 Lite Base?
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 Base?
For DeepSeek Coder v2 Lite Base, Q4_K_M (10.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (11.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek Coder v2 Lite Base on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run DeepSeek Coder v2 Lite Base. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 10.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run DeepSeek Coder v2 Lite Base locally?
Yes — DeepSeek Coder v2 Lite Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is DeepSeek Coder v2 Lite Base?
At Q4_K_M, DeepSeek Coder v2 Lite Base can reach ~174 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~243 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.7 GB active ÷ (8000 × 0.65) = 0.33169 ms, plus 27 layers × 0.055 ms = 1.48500 ms, so 1000 ÷ 1.81669 ms = ~550 tok/s
Estimated speed at Q4_K_M (10.2 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 v2 Lite Base?
At Q4_K_M, the download is about 9.42 GB. The full-precision BF16 version is 31.41 GB. The smallest option (Q2_K) is 6.68 GB.
Which GPUs can run DeepSeek Coder v2 Lite Base?
38 consumer GPUs can run DeepSeek Coder v2 Lite Base at Q4_K_M. 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 Base?
150 devices with unified memory can run DeepSeek Coder v2 Lite Base at Q4_K_M (10.2 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.