DeepSeek-R1-0528-Qwen3-8B GGUF size and VRAM requirements
DeepSeek-R1-0528-Qwen3-8B-GGUF is a large reasoning-focused model focused on complex reasoning & stem. With approximately 8.19 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the qwen3 architecture with a native context window of up to 131,072 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.
Under the hood it uses 36 transformer layers, a hidden size of 4,096, 32 attention heads. It uses grouped-query attention (32 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF locally at a 4,096-token context, its quantized versions need between 5.49 GB (Q3_K_L, lowest quality) and 9.47 GB (Q8_0, 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-R1-0528-Qwen3-8B-GGUF is about 0.56 GB, rising to roughly 18.0 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q6_K, needing about 7.63 GB. That means lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF include Q3_K_L, Q4_K_M, Q6_K, Q8_0. The model supports a native context length of up to 131,072 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 |
|---|---|---|---|---|---|---|---|
| Q3_K_L | 4.33 | Good | 4.13 GB | 0.56 GB | 5.49 GB | 96.9 t/s | Fits in VRAM |
| Q4_K_M | 4.91 | Good | 4.68 GB | 0.56 GB | 6.04 GB | 85.4 t/s | Fits in VRAM |
| Q6_K | 6.57 | Excellent | 6.26 GB | 0.56 GB | 7.63 GB | 63.9 t/s | Fits in VRAM |
| Q8_0 | 8.51 | Excellent | 8.11 GB | 0.56 GB | 9.47 GB | 6.2 t/s | Offload |
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 lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF?
lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF is a reasoning-focused model with 8.19 billion parameters, based on the qwen3 architecture. It is released under the mit license and distributed as GGUF files for local inference.
How much VRAM do you need to run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF?
You need about 5.49 GB of VRAM to run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF entirely on the GPU using the Q3_K_L quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF fully on the GPU using Q6_K (about 7.63 GB).
Can I run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF fully on the GPU using Q8_0 (about 9.47 GB).
Can I run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF fully on the GPU using Q8_0 (about 9.47 GB).
What context length does lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF support?
lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF supports a native context length of up to 131,072 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF?
For lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF, a strong default is Q4_K_M, which needs about 6.04 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.