DeepSeek
DeepSeek Coder V2 236B (236B parameters) requires approximately 204.1 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 21B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 235 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run DeepSeek Coder V2 236B on your machine.
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lms load DeepSeek-Coder-V2-Instruct && lms server startQuick specs
About this model
Related models
Inference speed
Estimated decode speed (tokens/sec) for DeepSeek Coder V2 236B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~12 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 11.7 | Too big |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 6.1 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 5.7 | Too big |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 4.5 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 4.5 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 3.4 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 3.3 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 3.1 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.9 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.7 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.3 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.2 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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Quantization
How much VRAM DeepSeek Coder V2 236B (236B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~144 GB — about 43% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 92 GB | Low | Too big |
| Q3_K_S | 3 | 115.6 GB | Low | Too big |
| NVFP4 | 4 | 132.2 GB | Medium | Too big |
| Q4_K_Mrecommended | 4 | 144 GB | Medium | Too big |
| Q5_K_M | 5 | 169.9 GB | High | Too big |
| Q6_K | 6 | 193.5 GB | High | Too big |
| Q8_0 | 8 | 252.5 GB | Very High | Too big |
| F16 | 16 | 483.8 GB | Maximum | Too big |
VRAM shown is quantized weights only; add ~1–3 GB runtime overhead plus KV cache for your context length. Lower quants trade quality for memory — Q4_K_M is the usual sweet spot; Q2/Q3 only when you must fit a bigger model.
Quality benchmarks
Coding
Reasoning
Source: official · 2024-06-17
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
DeepSeek Coder V2 236B (236B parameters) requires approximately 204.1 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, AMD Instinct MI350X 288GB can run DeepSeek Coder V2 236B with a compatibility score of 93/100. It provides 288 GB of memory and achieves approximately 109.3 tokens per second.
The recommended quantization for DeepSeek Coder V2 236B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for DeepSeek Coder V2 236B: AMD Instinct MI350X 288GB (score: 93/100), AMD Instinct MI325X 256GB (score: 89/100), NVIDIA GB200 192GB (score: 79/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, DeepSeek Coder V2 236B is well-suited for code as well as reasoning. It was designed with these use cases in mind.
See also