DeepSeek-R1-Distill-Qwen-32B
32BDeepSeek
R1 distilled to 32B. Near-frontier reasoning on a single 24GB card (Q3/Q4).
131K
Max Context
3
Quant Variants
GGUF Q4_K_M
Best Quality
97.4%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = site benchmarks · Estimated = formula · Community = public reports
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Running DeepSeek-R1-Distill-Qwen-32B locally
At Q4_K_M and 4K of context, DeepSeek-R1-Distill-Qwen-32B needs about 21.7 GB — 18.7 GB of weights, 1.00 GB of KV cache and a 2.0 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 5090 at 32 GB, and 24 of the 63 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 7.00 GB, taking the total to 29.4 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 56 GB.
Which build to download
This index tracks 2 formats for it — GGUF, EXL2 — across 3 levels. Q4_K_M carries the lowest published perplexity loss at 2.6%. The fastest level measured here is EXL2 3.5bpw at 65 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.
Common questions
- How much VRAM does DeepSeek-R1-Distill-Qwen-32B need?
- About 21.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 29.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will DeepSeek-R1-Distill-Qwen-32B run on a 32GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 21.7 GB, which leaves 10.3 GB spare on a RTX 5090. That is the smallest card in this index that clears it comfortably; 24 of 63 do.
- Which quantization of DeepSeek-R1-Distill-Qwen-32B should I use?
- Q4_K_M has the lowest published quality loss (2.6%), and Q4_K_M is the level most people run. All 3 levels in the index are Q3_K_M, Q4_K_M, EXL2 3.5bpw.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 509032GB · +10.3Instinct MI100 32G32GB · +10.3Mac M4 Max 36G36GB · +5.3Mac M3 Pro 36G36GB · +5.3A100 40G40GB · +18.3
- Tight but possible
- RTX 409024GBRTX 309024GBTesla P40 24G24GB
- Compared here
- GGUF vs EXL2
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-09-21 RSS → /feed.xml