DeepSeek-R1-Distill-Llama-70B GGUF size and VRAM requirements
bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF is a very large reasoning-focused model with 70.55 billion parameters, built on the llama architecture. It has been downloaded 77,582 times.
To run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF locally at a 4,096-token context, its quantized versions need between 17.65 GB (IQ1_M, lowest quality) and 71.88 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ2_S, needing about 22.76 GB. That means bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF fits entirely in the VRAM of a 24 GB GPU or larger, running fully on the GPU.
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_M | 1.9 | Very low | 15.6 GB | 1.25 GB | 17.65 GB | 3.2 t/s | Offload |
| IQ2_XXS | 2.17 | Very low | 17.79 GB | 1.25 GB | 19.84 GB | 2.8 t/s | Offload |
| IQ2_XS | 2.4 | Very low | 19.69 GB | 1.25 GB | 21.74 GB | 2.5 t/s | Offload |
| IQ2_S | 2.52 | Very low | 20.71 GB | 1.25 GB | 22.76 GB | 2.4 t/s | Offload |
| IQ2_M | 2.73 | Low | 22.46 GB | 1.25 GB | 24.51 GB | — | Insufficient |
| Q2_K | 2.99 | Low | 24.56 GB | 1.25 GB | 26.61 GB | — | Insufficient |
| Q2_K_L | 3.11 | Low | 25.52 GB | 1.25 GB | 27.57 GB | — | Insufficient |
| IQ3_XXS | 3.11 | Low | 25.58 GB | 1.25 GB | 27.63 GB | — | Insufficient |
| Q3_K_S | 3.51 | Fair | 28.79 GB | 1.25 GB | 30.84 GB | — | Insufficient |
| IQ3_M | 3.62 | Fair | 29.74 GB | 1.25 GB | 31.79 GB | — | Insufficient |
| Q3_K_M | 3.89 | Fair | 31.91 GB | 1.25 GB | 33.96 GB | — | Insufficient |
| Q3_K_L | 4.21 | Good | 34.59 GB | 1.25 GB | 36.64 GB | — | Insufficient |
| IQ4_XS | 4.3 | Good | 35.3 GB | 1.25 GB | 37.35 GB | — | Insufficient |
| Q3_K_XL | 4.32 | Good | 35.45 GB | 1.25 GB | 37.5 GB | — | Insufficient |
| IQ4_NL | 4.54 | Good | 37.3 GB | 1.25 GB | 39.35 GB | — | Insufficient |
| Q4_0 | 4.55 | Good | 37.36 GB | 1.25 GB | 39.41 GB | — | Insufficient |
| Q4_K_S | 4.57 | Good | 37.58 GB | 1.25 GB | 39.63 GB | — | Insufficient |
| Q4_K_M | 4.82 | Good | 39.6 GB | 1.25 GB | 41.65 GB | — | Insufficient |
| Q4_1 | 5.02 | Very good | 41.27 GB | 1.25 GB | 43.32 GB | — | Insufficient |
| Q5_K_S | 5.52 | Very good | 45.32 GB | 1.25 GB | 47.37 GB | — | Insufficient |
| Q5_K_M | 5.66 | Very good | 46.52 GB | 1.25 GB | 48.57 GB | — | Insufficient |
| Q6_K | 6.56 | Excellent | 53.91 GB | 1.25 GB | 55.96 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 69.83 GB | 1.25 GB | 71.88 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF?
You need about 22.76 GB of VRAM to run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF entirely on the GPU using the IQ2_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF on an 8 GB GPU?
Partially. bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with IQ2_S), which runs but is slower.
Can I run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF on a 16 GB GPU?
Partially. bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q5_K_S), which runs but is slower.
Can I run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF fully on the GPU using IQ2_S (about 22.76 GB).
What is the best quantization for bartowski/DeepSeek-R1-Distill-Llama-70B-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.