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 Q4_K_M it needs about 41.30 GB of VRAM — see which GPUs and Macs can run it below.
1.6K 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 29.6 GB | 30.4 GB | 28.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 33.8 GB | 34.6 GB | 32.66 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 41.3 GB | 42.1 GB | 40.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 48.8 GB | 49.6 GB | 47.74 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 56.4 GB | 57.2 GB | 55.27 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 68.1 GB | 68.9 GB | 67.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 135.1 GB | 135.9 GB | 134.00 GB | Brain floating point 16 — preferred for training |
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 Llm 67B Chat?
Q4_K_M · 41.3 GBDeepseek Llm 67B Chat (Q4_K_M) requires 41.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 54+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 42.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Deepseek Llm 67B Chat?
Q4_K_M · 41.3 GB27 devices with unified memory can run Deepseek Llm 67B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Benchmarks
Benchmark details →Frequently Asked Questions
- How much VRAM does Deepseek Llm 67B Chat need?
Deepseek Llm 67B Chat requires 41.3 GB of VRAM at Q4_K_M, or 135.1 GB at BF16. Full 4K context adds up to 0.8 GB (42.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 67B × 4.8 bits ÷ 8 = 40.2 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
Q4_K_M41.3 GBQ4_K_M + full context42.1 GB- Can NVIDIA GeForce RTX 5090 run Deepseek Llm 67B Chat?
Yes, at Q2_K (29.6 GB) or lower. Higher quantizations like Q3_K_M (33.8 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
- What's the best quantization for Deepseek Llm 67B Chat?
For Deepseek Llm 67B Chat, Q4_K_M (41.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (48.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 29.6 GB.
VRAM requirement by quantization
Q2_K29.6 GBQ4_K_M ★41.3 GBQ5_K_M48.8 GBQ6_K56.4 GBQ8_068.1 GBBF16135.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Deepseek Llm 67B Chat on a Mac?
Deepseek Llm 67B Chat requires at least 29.6 GB at Q2_K, 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 Q4_K_M quantization it needs 41.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Llm 67B Chat?
At Q4_K_M, Deepseek Llm 67B Chat can reach ~107 tok/s on AMD Instinct MI350X. 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 ÷ 41.3 × 0.65 = ~126 tok/s
Estimated speed at Q4_K_M (41.3 GB)
~126 tok/s~126 tok/s~107 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 Q4_K_M, the download is about 40.20 GB. The full-precision BF16 version is 134.00 GB. The smallest option (Q2_K) is 28.48 GB.
- Which GPUs can run Deepseek Llm 67B Chat?
No single consumer GPU has enough VRAM to run Deepseek Llm 67B Chat at Q4_K_M (41.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Deepseek Llm 67B Chat?
27 devices with unified memory can run Deepseek Llm 67B Chat at Q4_K_M (41.3 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.