DeepSeek R1 GGUF Size & VRAM Requirements
DeepSeek-R1-GGUF is a very large reasoning-focused model focused on complex reasoning & stem. With approximately 671.03 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the deepseek2 architecture with a native context window of up to 163,840 tokens. The model is primarily recommended for multi-step logic deductions, mathematical problem solving, chain-of-thought analysis, and scientific research. Released under the mit license, it can be executed fully offline without sending data to external APIs.
DeepSeek-R1-GGUF is a Mixture-of-Experts model with 256 experts, of which 8 are active on each token. Routing only 8 of 256 experts makes it noticeably faster than a dense model of the same size — but every expert still has to be held in memory, so the numbers below are set by the full parameter count, not the active one. It is built from 61 transformer layers, a hidden size of 7,168, 128 attention heads. DeepSeek-R1-GGUF uses Multi-head Latent Attention, caching a single compressed latent per layer instead of full keys and values. That keeps the KV cache small even at long context — the memory figures on this page already reflect it.
To run unsloth/DeepSeek-R1-GGUF locally at a 4,096-token context, its quantized versions need between 131.67 GB (IQ1_S, lowest quality) and 1251.16 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for DeepSeek-R1-GGUF is about 0.27 GB, rising to roughly 10.72 GB at its full 163,840-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for unsloth/DeepSeek-R1-GGUF include IQ1_S, IQ1_M, IQ2_XXS, Q2_K_XS, Q2_K_XL, Q2_K, Q2_K_L, Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0, BF16. The model supports a native context length of up to 163,840 tokens; a longer context grows the KV cache and the memory needed.
Recommended Use Cases & Local Setup
Ideal for: Multi-step logic deductions, mathematical problem solving, chain-of-thought analysis, and scientific research.
Native support for collapsing and viewing multi-step <think> reasoning tokens
Rich web interface supporting reasoning models with system prompts disabled
High-throughput local API backend
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ1_S | 1.67 | Very low | 130.6 GB | 0.27 GB | 131.67 GB | — | Insufficient |
| IQ1_M | 2.01 | Very low | 157.32 GB | 0.27 GB | 158.38 GB | — | Insufficient |
| IQ2_XXS | 2.34 | Very low | 182.69 GB | 0.27 GB | 183.76 GB | — | Insufficient |
| Q2_K_XS | 2.64 | Low | 206.06 GB | 0.27 GB | 207.13 GB | — | Insufficient |
| Q2_K_XL | 2.7 | Low | 211.04 GB | 0.27 GB | 212.11 GB | — | Insufficient |
| Q2_K | 2.91 | Low | 227.27 GB | 0.27 GB | 228.34 GB | — | Insufficient |
| Q2_K_L | 2.91 | Low | 227.47 GB | 0.27 GB | 228.54 GB | — | Insufficient |
| Q3_K_M | 3.81 | Fair | 297.28 GB | 0.27 GB | 298.34 GB | — | Insufficient |
| Q4_K_M | 4.82 | Good | 376.65 GB | 0.27 GB | 377.72 GB | — | Insufficient |
| Q5_K_M | 5.67 | Very good | 442.75 GB | 0.27 GB | 443.82 GB | — | Insufficient |
| Q6_K | 6.57 | Excellent | 512.97 GB | 0.27 GB | 514.04 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 664.3 GB | 0.27 GB | 665.37 GB | — | Insufficient |
| BF16 | 16.0 | Excellent | 1250.09 GB | 0.27 GB | 1251.16 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is unsloth/DeepSeek-R1-GGUF?
unsloth/DeepSeek-R1-GGUF is a reasoning-focused model with 671.03 billion parameters, based on the deepseek2 architecture. It is released under the mit license and distributed as GGUF files for local inference.
Is unsloth/DeepSeek-R1-GGUF a Mixture-of-Experts (MoE) model?
Yes. unsloth/DeepSeek-R1-GGUF is a Mixture-of-Experts model with 256 experts, of which 8 are activated per token. That makes it faster than a dense model of the same size, but all 256 experts must be loaded into memory, so the VRAM/RAM it needs is driven by the total parameter count, not the active one.
How does unsloth/DeepSeek-R1-GGUF handle long context?
unsloth/DeepSeek-R1-GGUF uses Multi-head Latent Attention, which caches one compressed latent per layer instead of full keys and values. Its KV cache therefore grows much more slowly than a standard model of the same size, so long contexts add relatively little memory.
Can I run unsloth/DeepSeek-R1-GGUF on an 8 GB GPU?
No. unsloth/DeepSeek-R1-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run unsloth/DeepSeek-R1-GGUF on a 16 GB GPU?
No. unsloth/DeepSeek-R1-GGUF does not fit on a 16 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run unsloth/DeepSeek-R1-GGUF on a 24 GB GPU?
No. unsloth/DeepSeek-R1-GGUF does not fit on a 24 GB GPU, even with the smallest quantization and system RAM offloading.
What context length does unsloth/DeepSeek-R1-GGUF support?
unsloth/DeepSeek-R1-GGUF supports a native context length of up to 163,840 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for unsloth/DeepSeek-R1-GGUF?
For unsloth/DeepSeek-R1-GGUF, a strong default is Q4_K_M, which needs about 377.72 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.