DeepSeek-R1-Distill-Qwen-7B
7BDeepSeek
R1 reasoning in a 7B footprint. Best value for 8–12GB VRAM CoT experiments.
131K
Max Context
2
Quant Variants
EXL2 4.65bpw
Best Quality
+2.2%
Perplexity rise (best build)
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
| Format | Level | BPW | VRAM | PPL Loss | Speed | Source | Actions |
|---|---|---|---|---|---|---|---|
| GGUF | Q4_K_M | 4.85 | 5.1 GB | 3.0% | 152 tok/s | Estimated | |
| EXL2 | 4.65bpw | 4.65 | 4.9 GB | 2.2% | 210 tok/s | Estimated |
Which file each size is for
A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.
- GGUF Q4_K_MSized at the calculator's generic rate for this level; no file was checked.
- EXL2 4.65bpwSized at the calculator's generic rate for this level; no file was checked.
Similar models
Compare with DeepSeek-R1-Distill-Llama-8BDeepSeek-R1-Distill-Llama-8B
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R1 reasoning distilled into Llama 3.1 8B. Best chain-of-thought for 8–12GB cards; huge community GGUF support.
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Running DeepSeek-R1-Distill-Qwen-7B locally
At Q4_K_M and 4K of context, DeepSeek-R1-Distill-Qwen-7B needs about 5.1 GB — 4.4 GB of weights, 0.22 GB of KV cache and a 0.5 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 11 of them, from the Mac M1 8G up — and 78 of the 78 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 1.53 GB, taking the total to 6.8 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 13 GB.
Which build to download
This index tracks 2 formats for it — GGUF, EXL2 — across 2 levels. EXL2 4.65bpw carries the lowest published perplexity loss at 2.2%. The fastest level listed is EXL2 4.65bpw at about 210 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. 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 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does DeepSeek-R1-Distill-Qwen-7B need?
- About 5.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 6.8 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-7B run on a 8GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 5.1 GB, which leaves 0.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.
- Which quantization of DeepSeek-R1-Distill-Qwen-7B should I use?
- EXL2 4.65bpw has the lowest published quality loss (2.2%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, EXL2 4.65bpw.
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
- Mac M1 8G8GB · +0.9Mac M3 8G8GB · +0.9RTX 40608GB · +2.9RTX 4060 Ti 8G8GB · +2.9Radeon RX 9060 XT 8G8GB · +2.9
- 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 2026-11-07 RSS → /feed.xml