Deepseek Llm 67B Chat — Hardware Requirements & GPU Compatibility
Chat
Deepseek Llm 67B Chat is a 67B-parameter open language model from DeepSeek in the DeepSeek family. It supports a context window of up to 4,096 tokens. At BF16 it needs about 135.10 GB of VRAM — see which GPUs and Macs can run it below.
1.5K downloads
207 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek
- Parameters
- 67B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2023-11-29
- License
- Other
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HuggingFace
How Much VRAM Does Deepseek Llm 67B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 135.1 GB | 135.9 GB | 134.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Deepseek Llm 67B Chat?
BF16 · 135.1 GBDeepseek Llm 67B Chat (BF16) requires 135.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 176+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 135.9 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Deepseek Llm 67B Chat?
BF16 · 135.1 GB4 devices with unified memory can run Deepseek Llm 67B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
View all 2 →Related Models
Frequently Asked Questions
- How much VRAM does Deepseek Llm 67B Chat need?
Deepseek Llm 67B Chat requires 135.1 GB of VRAM at BF16. Full 4K context adds up to 0.8 GB (135.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 67B × 16 bits ÷ 8 = 134 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.9 GB (at full 4K context)
VRAM usage by quantization
BF16135.1 GBBF16 + full context135.9 GB- Can NVIDIA GeForce RTX 5090 run Deepseek Llm 67B Chat?
No — Deepseek Llm 67B Chat requires at least 135.1 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Deepseek Llm 67B Chat on a Mac?
Deepseek Llm 67B Chat requires at least 135.1 GB at BF16, 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 Llm 67B Chat locally?
Yes — Deepseek Llm 67B Chat can run locally on consumer hardware. At BF16 quantization it needs 135.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Llm 67B Chat?
At BF16, Deepseek Llm 67B Chat can reach ~22 tok/s on AMD Instinct MI300X. 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 ÷ 135.1 × 0.55 = ~22 tok/s
Estimated speed at BF16 (135.1 GB)
~22 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Deepseek Llm 67B Chat?
At BF16, the download is about 134.00 GB.
- Which GPUs can run Deepseek Llm 67B Chat?
No single consumer GPU has enough VRAM to run Deepseek Llm 67B Chat at BF16 (135.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run Deepseek Llm 67B Chat?
4 devices with unified memory can run Deepseek Llm 67B Chat at BF16 (135.1 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.