DeepSeek R1 Distill Llama 8B — Hardware Requirements & GPU Compatibility
ChatReasoning
DeepSeek R1 Distill Llama 8B brings R1's reinforcement-learned reasoning capabilities to the widely supported Llama 3.1 8B architecture. By distilling the full 684.5B R1 model's reasoning patterns into this 8 billion parameter dense model, DeepSeek created a version that benefits from the extensive Llama ecosystem of tools, quantizations, and inference engines. For users who prefer the Llama architecture or already have tooling built around it, this model offers a plug-and-play path to chain-of-thought reasoning. Its hardware requirements are very approachable, running well on consumer GPUs with 8 GB or more of VRAM at common quantization levels.
857.1K downloads
850 likesSpecifications
- Publisher
- DeepSeek
- Family
- Llama
- Parameters
- 8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-02-24
- License
- MIT
Get Started
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 |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 2.8 GB | 19.7 GB | 2.20 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 3.3 GB | 20.2 GB | 2.70 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 3.7 GB | 20.6 GB | 3.10 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 3.9 GB | 20.8 GB | 3.30 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 4.0 GB | 20.9 GB | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 21.0 GB | 3.50 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.2 GB | 21.1 GB | 3.60 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.5 GB | 21.4 GB | 3.90 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 21.5 GB | 4.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.7 GB | 21.6 GB | 4.10 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.9 GB | 21.8 GB | 4.30 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 5.1 GB | 22.0 GB | 4.50 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 5.1 GB | 22.0 GB | 4.50 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 5.1 GB | 22.0 GB | 4.50 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 5.5 GB | 22.4 GB | 4.90 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 6.1 GB | 23.0 GB | 5.50 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 6.4 GB | 23.3 GB | 5.80 GB | 5-bit large quantization |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 25.5 GB | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DeepSeek R1 Distill Llama 8B?
Q4_K_M · 5.4 GBDeepSeek R1 Distill Llama 8B (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.3 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?
Q4_K_M · 5.4 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 (8)
Frequently Asked Questions
- How much VRAM does DeepSeek R1 Distill Llama 8B need?
DeepSeek R1 Distill Llama 8B requires 5.4 GB of VRAM at Q4_K_M, or 8.6 GB at Q8_0. Full 131K context adds up to 16.9 GB (22.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 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
Q4_K_M5.4 GBQ4_K_M + full context22.3 GB- What's the best quantization for DeepSeek R1 Distill Llama 8B?
For DeepSeek R1 Distill Llama 8B, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.8 GB.
VRAM requirement by quantization
IQ2_XXS2.8 GB~53%Q3_K_S4.1 GB~77%IQ4_XS4.9 GB~87%Q4_K_M ★5.4 GB~89%Q4_K_L5.5 GB~90%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 2.8 GB at IQ2_XXS, 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 Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek R1 Distill Llama 8B?
At Q4_K_M, DeepSeek R1 Distill Llama 8B can reach ~543 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~122 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 ÷ 5.4 × 0.55 = ~543 tok/s
Estimated speed at Q4_K_M (5.4 GB)
AMD Instinct MI300X~543 tok/sNVIDIA GeForce RTX 4090~122 tok/sNVIDIA H100 SXM~406 tok/sAMD Instinct MI250X~336 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 Q4_K_M, the download is about 4.80 GB. The full-precision Q8_0 version is 8.00 GB. The smallest option (IQ2_XXS) is 2.20 GB.