Models › DeepSeek-R1 family

DeepSeek-R1-Zero

deepseek-ai MIT out 21 months ago

684B parameters (8 of 256 experts per token), text in, text out, 160K tokens of context.

Verified 2026-10-25 Rank #553 in LLMs · score 74.9 method v1 Sources 3 Downloads 30d 4.8k · #546 of 619 tracked

Can I use it?MITPermissivelicence file2026-10-24
Do people use it?4.8kdownloads, 30 days#546 of 619 we track · below the median+4 likes, downloads −20% this week982 likesHugging Face2026-10-24

Worth reading

Picked by rule from 32 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 1,843 HN pts
github.com 2025-01-20 1,843 HN pts · 663 comments 2.1k words about the DeepSeek R1 family family
Latest · 6 months ago

ZAYA1-8B matches DeepSeek-R1 on math with less than 1B active parameters

Zyphra released ZAYA1-8B, a model that matches DeepSeek-R1 on math benchmarks while using less than 1 billion active parameters.

  • The model was trained entirely on a 1,024 node AMD cluster built with IBM using AMD Pensando Pollara interconnect.
  • Zyphra trained ZAYA1-8B end to end on AMD Instinct MI300X GPUs for pretraining, midtraining, and supervised fine-tuning.
firethering.com 2026-05-07 117 HN pts · 56 comments 1.3k words about the DeepSeek R1 family family

25 best of 32 tracked

The 2 picks above, plus these 23. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. PaperDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RLarxiv.org · 2025-01-25 · 1,351 HN pts
  2. ArticlePromising results from DeepSeek R1 for codesimonwillison.net · 2025-01-28 · 979 HN pts
  3. ArticleRun DeepSeek R1 Dynamic 1.58-bitunsloth.ai · 2025-01-28 · 767 HN pts
  4. ArticleOpen-R1: an open reproduction of DeepSeek-R1huggingface.co · 2025-01-28 · 394 HN pts
  5. CodeDeepseek R1 Distill 8B Q40 on 4 x Raspberry Pi 5github.com · 2025-02-15 · 306 HN pts
  6. ArticleHow to Run DeepSeek R1 671B Locally on a $2000 EPYC Serverdigitalspaceport.com · 2025-02-01
  7. CodeDeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on Xeongithub.com · 2025-03-06
  8. ArticleThe Illustrated DeepSeek-R1newsletter.languagemodels.co · 2025-01-27 · 578 HN pts
  9. ArticleDeepSeek R1 Is Now Available on Azure AI Foundry and GitHubazure.microsoft.com · 2025-01-29 · 109 HN pts
  10. DiscussionComplete hardware and software setup for running Deepseek-R1 locallytwitter.com · 2025-01-29 · 250 HN pts
  11. CodeTinyZero: Reproduction of DeepSeek R1 Zero in countdown and multiplication tasksgithub.com · 2025-01-25
  12. CodeOpen Reproduction of DeepSeek-R1github.com · 2025-01-27
  13. ArticleOfficial DeepSeek R1 Now on Ollamaollama.com · 2025-01-20
  14. ArticleBerkeley Researchers Replicate DeepSeek R1's Core Tech for Just $30: A Small Modxyzlabs.substack.com · 2025-01-28
  15. CodeImplementing DeepSeek R1's GRPO algorithm from scratchgithub.com · 2025-04-13
  16. ArticleMini-R1: Reproduce DeepSeek R1 "Aha Moment"philschmid.de · 2025-01-31
  17. DiscussionDeepSeek R1 671B running on 2 M2 Ultras faster than reading speedtwitter.com · 2025-01-29
  18. ArticleHow to run DeepSeek R1 locallyworkos.com · 2025-01-29
  19. ArticleHow to Run DeepSeek R1 Distilled Reasoning Models on RyzenAI and Radeon GPUsguru3d.com · 2025-02-02
  20. ArticleA new, faster DeepSeek R1-0528 variant appears from German labventurebeat.com · 2025-07-05
  21. ArticleRunning DeepSeek R1 Models Locally on NPUblogs.windows.com · 2025-02-01
  22. ArticleExperience the DeepSeek R1 Distilled 'Reasoning' Models on Ryzen AI and Radeoncommunity.amd.com · 2025-02-04
  23. ArticleDeepSeek R1 is far better than OpenAI o1 at finding bugs in pull requestsgreptile.com · 2025-01-30

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 6 quantized builds and 66 Spaces.

  • As a service0no API provider listed
  • On own hardware6quantized builds
  • Built on1211 fine-tunes · 1 adapters · 0 merges
  • In apps66Hugging Face Spaces

Top Spaces running it: llamameta/DeepSeek-R1-Chat-Assistant-Web-Search ♥ 260 · KBaba7/Quant ♥ 51 · ruslanmv/DeepSeek-R1-Chatbot ♥ 36 · Sarath0x8f/Document-QA-bot ♥ 33

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Needs an 8×80 GB node even at INT4

About 411 GB at INT4, 821 GB at FP8, 1.64 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 411 GB of weights (estimate; KV cache and activations extra)INT4411 GBFP8: about 821 GB of weights (estimate; KV cache and activations extra)FP8821 GBBF16: about 1.64 TB of weights (estimate; KV cache and activations extra)BF161.64 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 411 GB of weights (estimate; KV cache and activations extra)INT4411 GBFP8: about 821 GB of weights (estimate; KV cache and activations extra)FP8821 GBBF16: about 1.64 TB of weights (estimate; KV cache and activations extra)BF161.64 TB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2025-01-20), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

18 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

18 derivatives on the Hub, most of them fine-tunes

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

6 quantized (Hugging Face model tree)11 fine-tunes (Hugging Face model tree)1 adapter (Hugging Face model tree)6 quantized11 fine-tunes1 adapter
6 quantized (Hugging Face model tree)11 fine-tunes (Hugging Face model tree)1 adapter (Hugging Face model tree)6 quantized11 fine-tunes1 adapter

All variants →

Reference

Everything else, folded: the full tables, every source.

Use-case evidence

4 use cases

What it is good at 4 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

22 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesDeepseekV3ForCausalLM
Model typedeepseek_v3
Dtypebfloat16
Params by dtypeBF16: 3,918,786,560 · F32: 15,104 · F8_E4M3: 680,571,043,840
Size bandXXL
MoEyes
Experts256
Experts per token8
Context length163,840 (config.json max_position_embeddings)
PapersarXiv:2501.12948
LicenseMIT (mit)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · DeepseekV3ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · DeepseekV3ForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for DeepSeek-R1-Zero: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.64 TB · Cluster (≥640 GB)
FP8 (1 byte/param)821 GB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)411 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

18 variants

Lineage

Original release (shipped quantized by the lab) Family: deepseek-ai/DeepSeek-R1 18 derivatives on the Hub

quantized: 6finetune: 11adapter: 1

Top variants (18 shown, ranked by downloads)

Relation
Quant
18 rows
Variant Relation Quant Params Band License Author Likes Downloads
unsloth/DeepSeek-R1-Zero-GGUF quantized GGUF — — mit unsloth 27 40.8k
DevQuasar-3/deepseek-ai.DeepSeek-R1-Zero-GGUF quantized GGUF — — — DevQuasar-3 2 3.9k
unsloth/DeepSeek-R1-Zero quantized — 685B XXL mit unsloth 6 2.1k
Daemontatox/Zireal-0 finetune — 684B XXL apache-2.0 Daemontatox 1 2.2k
opensourcerelease/DeepSeek-R1-Zero-bf16 finetune -bf16 684B XXL mit opensourcerelease 0 587
gghfez/DeepSeek-R1-Zero-256x21B-BF16 finetune -BF16 — — mit gghfez 0 422
gghfez/DeepSeek-R1-Zero-IQ2_KS quantized GGUF — — mit gghfez 0 292
gghfez/DeepSeek-R1-Zero-IQ3_KS quantized GGUF — — mit gghfez 0 288
adamo1139/DeepSeek-R1-Zero-AWQ quantized AWQ 671B XXL mit adamo1139 0 213
mradermacher/DeepSeek-R1-Zero-GGUF finetune GGUF — — mit mradermacher 1 0
mradermacher/DeepSeek-R1-Zero-i1-GGUF finetune GGUF — — mit mradermacher 1 0
osllmai-community/DeepSeek-R1-Zero-GGUF finetune GGUF — — mit osllmai-community 0 0
Oi2qtop/sigma finetune — — — — Oi2qtop 0 0
Elchapofx28/Girl finetune — — — afl-3.0 Elchapofx28 0 0
Xacales/code1 finetune — — — gfdl Xacales 0 0
saha78rus/Realistic finetune — — — apache-2.0 saha78rus 0 0
Etan498/inferno finetune — — — unknown Etan498 0 0
shynword/CHATGPT313 adapter — — — apache-2.0 shynword 0 0

Repository changelog

27 commits

Repository changelog 27 commits

DateCommitSHA
2025-03-27 Small fix 7223428
2025-02-24 Update bib info 9d8dd91
2025-02-09 Update tokenizer_config.json 978b97c
2025-02-01 Update README.md b3c33b0
2025-01-26 Update README.md d9c339c
2025-01-23 Update README.md b039779
2025-01-22 Update tokenizer_config.json 19de63b
2025-01-22 Update tokenizer_config.json d3cf750
2025-01-21 Update README.md (#12) 9cc4dfb
2025-01-21 add library name & auto-tag (#10) 57b1029
2025-01-21 Add generation_config.json 7c4fde6
2025-01-20 Release DeepSeek-R1 cfa4541
2025-01-20 Add files using upload-large-folder tool 3a13570
2025-01-20 Add files using upload-large-folder tool 4a087ad
2025-01-20 Add files using upload-large-folder tool 5f6c31f
2025-01-20 Add files using upload-large-folder tool 6c57e3e
2025-01-20 Add files using upload-large-folder tool acd6df8
2025-01-20 Add files using upload-large-folder tool d511d25
2025-01-20 Add files using upload-large-folder tool 16842fd
2025-01-20 Add files using upload-large-folder tool 8c9cab6
2025-01-20 Add files using upload-large-folder tool 6b06717
2025-01-20 Add files using upload-large-folder tool 2aafb87
2025-01-20 Add files using upload-large-folder tool 60c0395
2025-01-20 Add files using upload-large-folder tool fb5c8d8
2025-01-20 Add files using upload-large-folder tool e30d997
2025-01-20 Add files using upload-large-folder tool a0ba933
2025-01-20 initial commit 24dee08

Articles and coverage

32 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Analysis9

firethering.com2026-05-07HN front pageabout the DeepSeek R1 familyHN 117 pts · 56 comments1,293 words
Zyphra released ZAYA1-8B, a model that matches DeepSeek-R1 on math benchmarks while using less than 1 billion active parameters. The 8.4B parameter mixture of experts model activates only 760M parameters at inference and employs Markovian RSA to scale performance with compute budget. It requires a specific vLLM fork for local deployment and is available under an Apache 2.0 license.written, reviewed, cited
  • The model was trained entirely on a 1,024 node AMD cluster built with IBM using AMD Pensando Pollara interconnect.
  • Zyphra trained ZAYA1-8B end to end on AMD Instinct MI300X GPUs for pretraining, midtraining, and supervised fine-tuning.
  • The model requires Zyphra's own fork of vLLM to run properly because the standard vLLM install won't work.
arxiv.org2025-01-25authorityHN front pageabout the DeepSeek R1 familyHN 1,351 pts · 1,056 comments371 words
simonwillison.net2025-01-28authorityHN front pageabout the DeepSeek R1 familyHN 979 pts · 746 comments345 words
A PR for llama.cpp delivers x2 speed for WASM by optimizing SIMD instructions for qX_K_q8_K and qX_0_q8_0 dot product functions. Surprisingly, 99% of the code in this PR is written by DeekSeek-R1. The model spent 3-5 minutes thinking about each prompt to generate the optimization.written, reviewed, cited
github.com2025-01-20authorityHN front pageabout the DeepSeek R1 familyHN 1,843 pts · 663 comments2,107 words
unsloth.ai2025-01-28authorityHN front pageabout the DeepSeek R1 familyHN 767 pts · 332 comments
huggingface.co2025-01-28authorityHN front pageabout the DeepSeek R1 familyHN 394 pts · 234 comments890 words
github.com2025-02-15authorityHN front pageabout the DeepSeek R1 familyHN 306 pts · 156 comments766 words
newsletter.languagemodels.co2025-01-27HN front pageabout the DeepSeek R1 familyHN 578 pts · 118 comments1,715 words
azure.microsoft.com2025-01-29authorityHN front pageabout the DeepSeek R1 familyHN 109 pts · 49 comments

Community1

twitter.com2025-01-29HN front pageabout the DeepSeek R1 familyHN 250 pts · 192 comments
All coverage tracked for this model (32) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
ZAYA1-8B matches DeepSeek-R1 on math with less than 1B active parametersfirethering.com2026-05-072026-10-07hn
A new, faster DeepSeek R1-0528 variant appears from German labventurebeat.com2025-07-052026-10-07hn
Implementing DeepSeek R1's GRPO algorithm from scratchgithub.com2025-04-132026-10-07hn
DeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on Xeongithub.com2025-03-062026-10-07hn
Deepseek R1 Distill 8B Q40 on 4 x Raspberry Pi 5github.com2025-02-152026-10-07hn
DeepSeek-R1's strong zero-shot performance on agentic taskskrasserm.github.io2025-02-052026-10-07hn
Logic R1: Reproduce DeepSeek R1 Zero on 2K Logic Puzzle Datasetgithub.com2025-02-052026-10-07hn
Experience the DeepSeek R1 Distilled 'Reasoning' Models on Ryzen AI and Radeoncommunity.amd.com2025-02-042026-10-07hn
How to Run DeepSeek R1 Distilled Reasoning Models on RyzenAI and Radeon GPUsguru3d.com2025-02-022026-10-07hn
How to Run DeepSeek R1 671B Locally on a $2000 EPYC Serverdigitalspaceport.com2025-02-012026-10-07hn
Running DeepSeek R1 Models Locally on NPUblogs.windows.com2025-02-012026-10-07hn
How to run 1.58bit DeepSeek R1 with Open WebUIdocs.openwebui.com2025-01-312026-10-07hn
Mini-R1: Reproduce DeepSeek R1 "Aha Moment"philschmid.de2025-01-312026-10-07hn
Reproducing DeepSeek R1-Zero for < $30twitter.com2025-01-312026-10-07hn
DeepSeek R1 is far better than OpenAI o1 at finding bugs in pull requestsgreptile.com2025-01-302026-10-07hn
DeepSeek R1-Zero's self-verification and search abilities in just 3B parametersnitter.lucabased.xyz2025-01-302026-10-07hn
DeepSeek R1 Is Now Available on Azure AI Foundry and GitHubazure.microsoft.com2025-01-292026-10-07hn
How to run DeepSeek R1 locallyworkos.com2025-01-292026-10-07hn
Complete hardware and software setup for running Deepseek-R1 locallytwitter.com2025-01-292026-10-07hn
DeepSeek R1 671B running on 2 M2 Ultras faster than reading speedtwitter.com2025-01-292026-10-07hn
Berkeley Researchers Replicate DeepSeek R1's Core Tech for Just $30: A Small Modxyzlabs.substack.com2025-01-282026-10-07hn
Promising results from DeepSeek R1 for codesimonwillison.net2025-01-282026-10-07hn
Run DeepSeek R1 Dynamic 1.58-bitunsloth.ai2025-01-282026-10-07hn
Open-R1: an open reproduction of DeepSeek-R1huggingface.co2025-01-282026-10-07hn
The Illustrated DeepSeek-R1newsletter.languagemodels.co2025-01-272026-10-07hn
Open Reproduction of DeepSeek-R1github.com2025-01-272026-10-07hn
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RLarxiv.org2025-01-252026-10-07hn
TinyZero: Reproduction of DeepSeek R1 Zero in countdown and multiplication tasksgithub.com2025-01-252026-10-07hn
Deep Seek R1 on Hyperbolicnews.ycombinator.com2025-01-222026-10-07hn
Official DeepSeek R1 Now on Ollamaollama.com2025-01-202026-10-07hn
Deepseek R1 Zero learns to reason using reinforcement learning on base model [pdf]github.com2025-01-202026-10-07hn
DeepSeek-R1github.com2025-01-202026-10-07hn

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:40 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:12 UTC · Last verified 2026-10-25

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