LOADING DATASHEET
LOADING DATASHEET
by DeepSeek
RANKED #132 OF 318 ASSISTANTS · OVERALL #171 OF 494 RANKED · VIBE SCORE 5.4 · 30 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
DeepSeek-R1-Zero on Hugging Face (text generation). 7,215 downloads. Open weights for local or hosted use.
8 mentions
1 mentions
26 weeks · 9 voices in this window
TOO QUIET
Not enough weekly voices to call a trend yet.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download DeepSeek R1 Zero once and it's yours. No subscription, no rate limits, works offline.
671B parameters MoE (37B active), a Q4 file is about 404 GB
Mac Studio M2 Ultra (192GB Unified Memory) or 8x RTX 3090 (1
192 GB UNIFIED MEMORY / VRAM
~$5,500 USEDToo big for typical consumer hardware. Extremely low quants (UD-IQ1_S to IQ2_XXS, ~160-190 GB), ~4 tok/s, 4k context
Custom Server Rig with 8x NVIDIA RTX 4090 (24GB each)
192 GB TO 256 GB VRAM
~$18,000Q4_K_M or Q3_K quant, ~14 tok/s, 16k context
Enterprise AI Server with 8x NVIDIA H100 80GB
640 GB VRAM
~$280,000FP8 / 8-bit or full precision, ~40 tok/s, 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 Zero takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMMac Studio M2 Ultra (192GB Unified Memory) or 8x RTX 3090 (1 | SWEET SPOTCustom Server Rig with 8x NVIDIA RTX 4090 (24GB each) | FULL POWEREnterprise AI Server with 8x NVIDIA H100 80GB |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~2.1 MIN | ~36 S | ~13 S |
| Summarize a documenta long report boiled down to the points that matter | ~6.3 MIN★★★★★ | ~1.8 MIN★★★★★ | ~38 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~33 MIN★★★★★ | ~9.5 MIN★★★★★ | ~3.3 MIN★★★★★ |
| Build a backendan API with routes, storage and tests | ~1.7 HR★★★★★ | ~30 MIN★★★★★ | ~10 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~63 MIN★★★★★ | ~18 MIN★★★★★ | ~6.3 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.
The internet is split on DeepSeek R1 Zero. Biggest praise: getting facts right. Biggest gripe: price.
26 POSTS · 4 COMMENTS · REDDIT · X · HACKER NEWS · BLOG · GITHUB · STACK OVERFLOW · LEMMY
6 thumbs up · 2 thumbs down
1 thumbs up · 3 thumbs down
1 thumbs up · 2 thumbs down
Other models the crowd has fully reviewed, starting with text models like this one.
How was DeepSeek-R1 built; For dummies
Deepseek-r1-Zero is the most uncensored model
[D] r/MachineLearning - a year in review
DeepSeek-R1: How Did They Make an OpenAI-Level Reasoning Model So Damn Efficient?
DeepSeek R1-Zero Removes the Human Bottleneck
(from [8, 12, 14]) Reinforcement learning (RL) has played a crucial role throughout the history of research on large language models (LLMs). Through RL, we created early versions of instruction following models , made important advancements in alignment and…
It has been almost two years since OpenAI released o1, a model that popularized the idea of LLM-based reasoning models. DeepSeek-R1 followed about four months later, together with details of a reinforcement learning with verifiable rewards (RLVR) recipe to…
(from [1, 2, 3]) Scaling is one of the most impactful concepts in the history of AI research. For large language models (LLMs), scaling has mostly been studied in the context of pretraining, where rigorous scaling laws have allowed us to clearly define the…
(from [1, 3, 4]) Recent research on large language models (LLMs) has been heavily focused on reasoning and reinforcement learning (RL). At the center of this research lies Group Relative Policy Optimization (GRPO) [13], the RL optimizer used to train most…
(from [1, 19]) Reinforcement learning (RL) has always played a pivotal role in research on large language models (LLMs), beginning with its use for aligning LLMs to human preferences. More recently, researchers have heavily focused on using RL training to…
AI research team claims to reproduce DeepSeek core technologies for $30 — relatively small R1-Zero model has remarkable problem-solving abilities
Are LLMs Limited by Human Language?