DeepSeek R1 Distill Llama 8B — Hardware Requirements & GPU Compatibility
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2.6K downloads
109 likesSpecifications
- Publisher
- Unsloth
- Family
- Llama
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-07-18
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does DeepSeek R1 Distill Llama 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q5_K_S | 5.50 | 6.1 GB | 23 GB | 5.52 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 25.5 GB | 8.03 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DeepSeek R1 Distill Llama 8B?
Q5_K_M · 6.3 GBDeepSeek R1 Distill Llama 8B (Q5_K_M) requires 6.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 23.2 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run DeepSeek R1 Distill Llama 8B?
Q5_K_M · 6.3 GB33 devices with unified memory can run DeepSeek R1 Distill Llama 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does DeepSeek R1 Distill Llama 8B need?
DeepSeek R1 Distill Llama 8B requires 6.1 GB of VRAM at Q5_K_S, or 8.6 GB at Q8_0. Full 131K context adds up to 16.9 GB (23 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 5.5 bits ÷ 8 = 5.5 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
Q5_K_S6.1 GBQ5_K_S + full context23.0 GB- What's the best quantization for DeepSeek R1 Distill Llama 8B?
For DeepSeek R1 Distill Llama 8B, Q6_K (7.2 GB) offers the best balance of quality and VRAM usage. Q8_0 (8.6 GB) provides better quality if you have the VRAM. The smallest option is Q5_K_S at 6.1 GB.
VRAM requirement by quantization
Q5_K_S6.1 GB~92%Q5_K_M6.3 GB~92%Q6_K ★7.2 GB~95%Q8_08.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek R1 Distill Llama 8B on a Mac?
DeepSeek R1 Distill Llama 8B requires at least 6.1 GB at Q5_K_S, 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 8B locally?
Yes — DeepSeek R1 Distill Llama 8B can run locally on consumer hardware. At Q5_K_S quantization it needs 6.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek R1 Distill Llama 8B?
At Q5_K_S, DeepSeek R1 Distill Llama 8B can reach ~479 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~108 tok/s. 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 ÷ 6.1 × 0.55 = ~479 tok/s
Estimated speed at Q5_K_S (6.1 GB)
AMD Instinct MI300X~479 tok/sNVIDIA GeForce RTX 4090~108 tok/sNVIDIA H100 SXM~358 tok/sAMD Instinct MI250X~296 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 8B?
At Q5_K_S, the download is about 5.52 GB. The full-precision Q8_0 version is 8.03 GB.
- Which GPUs can run DeepSeek R1 Distill Llama 8B?
35 consumer GPUs can run DeepSeek R1 Distill Llama 8B at Q5_K_S (6.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run DeepSeek R1 Distill Llama 8B?
33 devices with unified memory can run DeepSeek R1 Distill Llama 8B at Q5_K_S (6.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.