DeepSeek R1 Distill Llama 70B Q2 K GGUF — Hardware Requirements & GPU Compatibility
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344 downloadsSpecifications
- Publisher
- roleplaiapp
- Family
- Llama
- Parameters
- 70B
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How Much VRAM Does DeepSeek R1 Distill Llama 70B Q2 K GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 32.7 GB | — | 29.75 GB | 2-bit quantization with K-quant improvements |
Which GPUs Can Run DeepSeek R1 Distill Llama 70B Q2 K GGUF?
Q2_K · 32.7 GBDeepSeek R1 Distill Llama 70B Q2 K GGUF (Q2_K) requires 32.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 43+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek R1 Distill Llama 70B Q2 K GGUF?
Q2_K · 32.7 GB13 devices with unified memory can run DeepSeek R1 Distill Llama 70B Q2 K GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroom
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Frequently Asked Questions
- How much VRAM does DeepSeek R1 Distill Llama 70B Q2 K GGUF need?
DeepSeek R1 Distill Llama 70B Q2 K GGUF requires 32.7 GB of VRAM at Q2_K.
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 3.4 bits ÷ 8 = 29.8 GB
KV Cache + Overhead ≈ 2.9 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q2_K32.7 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek R1 Distill Llama 70B Q2 K GGUF?
No — DeepSeek R1 Distill Llama 70B Q2 K GGUF requires at least 32.7 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run DeepSeek R1 Distill Llama 70B Q2 K GGUF on a Mac?
DeepSeek R1 Distill Llama 70B Q2 K GGUF requires at least 32.7 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 R1 Distill Llama 70B Q2 K GGUF locally?
Yes — DeepSeek R1 Distill Llama 70B Q2 K GGUF can run locally on consumer hardware. At Q2_K quantization it needs 32.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek R1 Distill Llama 70B Q2 K GGUF?
At Q2_K, DeepSeek R1 Distill Llama 70B Q2 K GGUF can reach ~89 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 ÷ 32.7 × 0.55 = ~89 tok/s
Estimated speed at Q2_K (32.7 GB)
AMD Instinct MI300X~89 tok/sNVIDIA H100 SXM~67 tok/sAMD Instinct MI250X~55 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of DeepSeek R1 Distill Llama 70B Q2 K GGUF?
At Q2_K, the download is about 29.75 GB.
- Which GPUs can run DeepSeek R1 Distill Llama 70B Q2 K GGUF?
No single consumer GPU has enough VRAM to run DeepSeek R1 Distill Llama 70B Q2 K GGUF at Q2_K (32.7 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek R1 Distill Llama 70B Q2 K GGUF?
13 devices with unified memory can run DeepSeek R1 Distill Llama 70B Q2 K GGUF at Q2_K (32.7 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.