Qwen2.5-Coder-32B GGUF size and VRAM requirements
Qwen2.5-Coder-32B-GGUF is a very large code-focused language model focused on coding & software engineering. With approximately 32.76 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the qwen2 architecture with a native context window of up to 32,768 tokens. The model is primarily recommended for automated code completion, bug triage, unit test generation, refactoring, and multi-file codebases. Released under the apache-2.0 license, it can be executed fully offline without sending data to external APIs.
Under the hood it uses 64 transformer layers, a hidden size of 5,120, 40 attention heads. It uses grouped-query attention (40 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run lmstudio-community/Qwen2.5-Coder-32B-GGUF locally at a 4,096-token context, its quantized versions need between 17.86 GB (Q3_K_L, lowest quality) and 34.23 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 Qwen2.5-Coder-32B-GGUF is about 1.0 GB, rising to roughly 8.0 GB at its full 32,768-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q4_K_M, needing about 20.29 GB. That means lmstudio-community/Qwen2.5-Coder-32B-GGUF fits entirely in the VRAM of a 24 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for lmstudio-community/Qwen2.5-Coder-32B-GGUF include Q3_K_L, Q4_K_M, Q6_K, Q8_0. The model supports a native context length of up to 32,768 tokens; a longer context grows the KV cache and the memory needed.
Recommended Use Cases & Local Setup
Ideal for: Automated code completion, bug triage, unit test generation, refactoring, and multi-file codebases.
Direct integration into VS Code and JetBrains IDEs for inline tab completion
Command-line AI pair programmer that automatically applies Git diffs
Local OpenAI-compatible API endpoint serving your editor
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 | 4.21 | Good | 16.06 GB | 1.0 GB | 17.86 GB | 3.1 t/s | Offload |
| Q4_K_M | 4.85 | Good | 18.49 GB | 1.0 GB | 20.29 GB | 2.7 t/s | Offload |
| Q6_K | 6.56 | Excellent | 25.04 GB | 1.0 GB | 26.84 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 32.43 GB | 1.0 GB | 34.23 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 lmstudio-community/Qwen2.5-Coder-32B-GGUF?
lmstudio-community/Qwen2.5-Coder-32B-GGUF is a code-focused language model with 32.76 billion parameters, based on the qwen2 architecture. It is released under the apache-2.0 license and distributed as GGUF files for local inference.
How much VRAM do you need to run lmstudio-community/Qwen2.5-Coder-32B-GGUF?
You need about 20.29 GB of VRAM to run lmstudio-community/Qwen2.5-Coder-32B-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/Qwen2.5-Coder-32B-GGUF on an 8 GB GPU?
Partially. lmstudio-community/Qwen2.5-Coder-32B-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
Can I run lmstudio-community/Qwen2.5-Coder-32B-GGUF on a 16 GB GPU?
Partially. lmstudio-community/Qwen2.5-Coder-32B-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
Can I run lmstudio-community/Qwen2.5-Coder-32B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmstudio-community/Qwen2.5-Coder-32B-GGUF fully on the GPU using Q4_K_M (about 20.29 GB).
What context length does lmstudio-community/Qwen2.5-Coder-32B-GGUF support?
lmstudio-community/Qwen2.5-Coder-32B-GGUF supports a native context length of up to 32,768 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/Qwen2.5-Coder-32B-GGUF?
For lmstudio-community/Qwen2.5-Coder-32B-GGUF, a strong default is Q4_K_M, which needs about 20.29 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.