Deepseek Coder 33B Instruct — Hardware Requirements & GPU Compatibility
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Deepseek Coder 33B Instruct is a 33.3B-parameter open language model from DeepSeek in the DeepSeek Coder family. It supports a context window of up to 16,384 tokens. At Q4_K_M it needs about 20.83 GB of VRAM — see which GPUs and Macs can run it below.
4.4K downloads
578 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 33.3B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,256
- Release Date
- 2023-11-01
- License
- Other
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HuggingFace
How Much VRAM Does Deepseek Coder 33B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.0 GB | 18.6 GB | 14.17 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.4 GB | 19.1 GB | 14.59 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 17.1 GB | 20.7 GB | 16.25 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.5 GB | 21.1 GB | 16.67 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.8 GB | 24.5 GB | 20.01 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 24.6 GB | 28.2 GB | 23.76 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 28.3 GB | 32.0 GB | 27.51 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 34.2 GB | 37.8 GB | 33.34 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 33B Instruct?
Q4_K_M · 20.8 GBDeepseek Coder 33B Instruct (Q4_K_M) requires 20.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 16K context window can add up to 3.6 GB, bringing total usage to 24.5 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Deepseek Coder 33B Instruct?
Q4_K_M · 20.8 GB41 devices with unified memory can run Deepseek Coder 33B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Deepseek Coder 33B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Deepseek Coder 33B Instruct need?
Deepseek Coder 33B Instruct requires 20.8 GB of VRAM at Q4_K_M, or 67.5 GB at BF16. Full 16K context adds up to 3.6 GB (24.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 33.3B × 4.8 bits ÷ 8 = 20 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 16K context)
VRAM usage by quantization
Q4_K_M20.8 GBQ4_K_M + full context24.5 GB- Can NVIDIA GeForce RTX 4090 run Deepseek Coder 33B Instruct?
Yes, at Q5_K_S (23.7 GB) or lower. Higher quantizations like Q5_K_M (24.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Deepseek Coder 33B Instruct?
For Deepseek Coder 33B Instruct, Q4_K_M (20.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.7 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 14.6 GB.
VRAM requirement by quantization
IQ3_XS14.6 GBIQ3_M15.8 GBIQ4_XS18.7 GBQ4_K_M ★20.8 GBQ5_K_S23.7 GBBF1667.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Deepseek Coder 33B Instruct on a Mac?
Deepseek Coder 33B Instruct requires at least 14.6 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 33B Instruct locally?
Yes — Deepseek Coder 33B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 20.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Coder 33B Instruct?
At Q4_K_M, Deepseek Coder 33B Instruct can reach ~211 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.8 × 0.65 = ~250 tok/s
Estimated speed at Q4_K_M (20.8 GB)
~250 tok/s~32 tok/s~250 tok/s~211 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 33B Instruct?
At Q4_K_M, the download is about 20.01 GB. The full-precision BF16 version is 66.69 GB. The smallest option (IQ3_XS) is 13.75 GB.
- Which GPUs can run Deepseek Coder 33B Instruct?
7 consumer GPUs can run Deepseek Coder 33B Instruct at Q4_K_M (20.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Deepseek Coder 33B Instruct?
41 devices with unified memory can run Deepseek Coder 33B Instruct at Q4_K_M (20.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.