LOADING DATASHEET
LOADING DATASHEET
by DeepSeek
RANKED #215 OF 271 ASSISTANTS · OVERALL #344 OF 6,567 · VIBE SCORE 5.4 · 34 VOICES
R1 reasoning distilled into Qwen 2.5 32B for efficient open-weight step-by-step problem solving
7 mentions
5 mentions
26 weeks · 23 voices
QUIETLY DEGRADING
Recent vibe and chatter are both sliding.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download Deepseek R1 Distill Qwen 32B once and it's yours. No subscription, no rate limits, works offline.
32.8B parameters, a Q4_K_M GGUF file is about 19.9 GB
NVIDIA GeForce RTX 3060 12GB plus 32GB System RAM
12 GB VRAM + 32 GB RAM
~$240 USEDQ4_K_M partial GPU offload with 8k context
NVIDIA GeForce RTX 3090 24GB
24 GB VRAM
~$700 USEDQ4_K_M fully in VRAM with 16k context
Dual NVIDIA GeForce RTX 3090 24GB (48GB total)
48 GB VRAM
~$1400 USEDQ8_0 or FP16 with full 32k to 64k context
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long Deepseek R1 Distill Qwen 32B takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce RTX 3060 12GB plus 32GB System RAM | SWEET SPOTNVIDIA GeForce RTX 3090 24GB | FULL POWERDual NVIDIA GeForce RTX 3090 24GB (48GB total) |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~83 S | ~21 S | ~13 S |
| Summarize a documenta long report boiled down to the points that matter | ~4.2 MIN★★★★★ | ~63 S★★★★★ | ~39 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~22 MIN★★★★★ | ~5.6 MIN★★★★★ | ~3.5 MIN★★★★★ |
| Build a backendan API with routes, storage and tests | ~69 MIN★★★★★ | ~17 MIN★★★★★ | ~11 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~42 MIN★★★★★ | ~10 MIN★★★★★ | ~6.6 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
Not enough discussion about Deepseek R1 Distill Qwen 32B yet to call it. We found 34 posts but people did not say much either way.
30 POSTS · 4 COMMENTS · REDDIT · GITHUB · STACK OVERFLOW · LEMMY
Other models the crowd has fully reviewed, starting with text models like this one.
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We open-sourced Chaperone-Thinking-LQ-1.0 — a 4-bit GPTQ + QLoRA fine-tuned DeepSeek-R1-32B that hits 84% on MedQA in ~20GB[N]
Context window length table
We open-sourced Chaperone-Thinking-LQ-1.0 — a 4-bit GPTQ + QLoRA fine-tuned DeepSeek-R1-32B that hits 84% on MedQA in ~20GB
8x AMD Instinct Mi60 Server + vLLM + unsloth/DeepSeek-R1-Distill-Qwen-32B FP16
A deepseek-v4-distill-qwen3.6-27b?
Reasoning models from Huggingface missing <thinking> tag
Is there a way to use DeepSeek-R1-Distill-Qwen-32B-unsloth-bnb-4bit by llama.cpp? How to convert it to GGUF without sacrificing accuracy? The current convert_hf_to_gguf.py script seems not support the conversion.
num_ctx parameter does not work