Models › DeepSeek family

deepseek-moe-16b-chat

deepseek-ai DeepSeek License (deepseek) out 3 years ago

16.4B parameters, text in, text out.

Verified 2026-10-25 Rank #683 in LLMs · score 72.4 method v1 Sources 3 Downloads 30d 24k · #443 of 619 tracked

Changed this week: 1 new quantized build on the Hub (17 in all).2026-10-24

Can I run it?9.8GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?DeepSeek License (deepseek)Attribution requiredlicence file
Do people use it?24kdownloads, 30 days#443 of 619 we track · below the median+3 likes, downloads +3% this week176 likesHugging Face

Worth reading

Picked by rule from 9 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 family family
Latest · 45 days ago
twitter.com 2026-09-10 1,016 HN pts · 574 comments about the DeepSeek family family

9 best of 9 tracked

The 2 picks above, plus these 7. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleDeepSeek V4 Flash 0731 Intelligence, Performance and Price Analysisartificialanalysis.ai · 2026-07-31 · 594 HN pts
  2. PaperDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RLarxiv.org · 2025-01-25 · 1,351 HN pts
  3. CodeDeepSeek releases Janus Pro, a text-to-image generator [pdf]github.com · 2025-01-27 · 912 HN pts
  4. CodeDeepSeek OCRgithub.com · 2025-10-20
  5. ArticleOpenAI says it has evidence DeepSeek used its model to train competitorft.com · 2025-01-29 · 747 HN pts
  6. CodeDeepSeek Open Infra: Open-Sourcing 5 AI Repos in 5 Daysgithub.com · 2025-02-21 · 757 HN pts
  7. CodeThe path to open-sourcing the DeepSeek inference enginegithub.com · 2025-04-14 · 550 HN pts

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: 17 quantized builds.

  • As a service0no API provider listed
  • On own hardware17quantized builds
  • Built on11 fine-tunes · 0 adapters · 0 merges
  • In apps0Hugging Face Spaces

Will it fit?

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

Fits a 16 GB card at INT4

About 9.8 GB at INT4, 20 GB at FP8, 39 GB 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 9.8 GB of weights (estimate; KV cache and activations extra)INT49.8 GBFP8: about 20 GB of weights (estimate; KV cache and activations extra)FP820 GBBF16: about 39 GB of weights (estimate; KV cache and activations extra)BF1639 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 9.8 GB of weights (estimate; KV cache and activations extra)INT49.8 GBFP8: about 20 GB of weights (estimate; KV cache and activations extra)FP820 GBBF16: about 39 GB of weights (estimate; KV cache and activations extra)BF1639 GB

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

The story so far

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

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 quantized

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

17 quantized (Hugging Face model tree)1 fine-tune (Hugging Face model tree)17 quantized1 fine-tune
17 quantized (Hugging Face model tree)1 fine-tune (Hugging Face model tree)17 quantized1 fine-tune

All variants →

Reference

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

Use-case evidence

1 use case

What it is good at 1 use case

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

15 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesDeepseekForCausalLM
Model typedeepseek
Params by dtypeBF16: 16,375,728,128
Size bandL
MoEyes
Experts per token6
PapersarXiv:2401.06066
LicenseDeepSeek License (deepseek) (other)
Hub snapshot2026-10-25

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for deepseek-moe-16b-chat: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)39 GB · Single high-end GPU (<80 GB)
FP8 (1 byte/param)20 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)9.8 GB · Single GPU (<24 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 model Family: deepseek-ai/DeepSeek 18 derivatives on the Hub

quantized: 17finetune: 1

Top variants (18 shown, ranked by downloads)

Relation
Quant
Size
18 rows
Variant Relation Quant Params Band License Author Likes Downloads
mradermacher/deepseek-moe-16b-chat-i1-GGUF quantized GGUF — — other mradermacher 0 19.6k
mradermacher/deepseek-moe-16b-chat-GGUF quantized GGUF — — other mradermacher 3 7.4k
aclava/deepseek-moe-16b-chat-Q4_K_M-GGUF quantized GGUF — — other aclava 1 5.7k
Lucy-in-the-Sky/deepseek-moe-16b-chat-Q4_K_M-GGUF quantized GGUF — — other Lucy-in-the-Sky 1 2.1k
Lucy-in-the-Sky/deepseek-moe-16b-chat-Q8_0-GGUF quantized Q8_0 — — other Lucy-in-the-Sky 0 1.9k
BlueMoonlight/deepseek-moe-16b-chat-mlx-4Bit quantized mlx 16.4B L other BlueMoonlight 0 1.6k
noctrex/DeepSeek-MoE-16B-Chat-MXFP4_MOE-GGUF quantized GGUF — — — noctrex 1 1.5k
amd/deepseek-moe-16b-chat-FP8-KV quantized -FP8 16.4B L other amd 0 864
Lucy-in-the-Sky/deepseek-moe-16b-chat-Q6_K-GGUF quantized Q6_K — — other Lucy-in-the-Sky 0 597
soyrsoyr/deepseek-moe-16b-chat-W4A16-GPTQ quantized -W4A16 2.56B M other soyrsoyr 0 439
Ibrahimarain/deepseek-moe-16b-chat-Q4_K_M-GGUF quantized GGUF — — other Ibrahimarain 0 355
Lucy-in-the-Sky/deepseek-moe-16b-chat-Q3_K_M-GGUF quantized GGUF — — other Lucy-in-the-Sky 0 316
soyrsoyr/deepseek-moe-16b-chat-W8A8-GPTQ quantized -W8A8 16.4B L other soyrsoyr 0 208
soyrsoyr/deepseek-moe-16b-chat-NVFP4-GPTQ quantized NVFP4 9.41B L other soyrsoyr 0 155
darkknight25/deepseek-16b-medical-GPT finetune — — — mit darkknight25 3 0
Lucy-in-the-Sky/deepseek-moe-16b-chat-Q2_K-GGUF quantized Q2_K — — other Lucy-in-the-Sky 0 142
MIThridate/DeepSeek-MoE-16B-Chat-SAMI-AsymQ quantized GGUF — — other MIThridate 0 92
soyrsoyr/deepseek-moe-16b-chat-FP8-GPTQ quantized -FP8 16.4B L other soyrsoyr 0 73

Repository changelog

1 change
changed in the last 7 daysOther: 2026-10-24all changes

Articles and coverage

9 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.

Analysis7

artificialanalysis.ai2026-07-31authorityHN front pageabout the DeepSeek familyHN 594 pts · 312 comments1,118 words
DeepSeek V4 Flash 0731 (Max) is a 284B parameter open-weight model released on July 31, 2026, under the MIT license. It scores 34 on the Artificial Analysis Intelligence Index while costing $0.14 per 1M input tokens and utilizing only 13B active parameters during inference. The model supports a 1M token context window and outputs text at 149.8 tokens per second.written, reviewed, cited
  • This model is deprecated and benchmarking continues only for the default 10k input token workload.
  • The model is notably fast but generates output that is very verbose compared to the median.
  • For a blended rate using a 7:2:1 cache hit/input/output ratio, the cost is $0.07 per 1M tokens.
arxiv.org2025-01-25authorityHN front pageabout the DeepSeek familyHN 1,351 pts · 1,056 comments371 words
github.com2025-01-20authorityHN front pageabout the DeepSeek familyHN 1,843 pts · 663 comments2,107 words
github.com2025-01-27authorityHN front pageabout the DeepSeek familyHN 912 pts · 674 comments
ft.com2025-01-29HN front pageabout the DeepSeek familyHN 747 pts · 1,541 comments
github.com2025-02-21authorityHN front pageabout the DeepSeek familyHN 757 pts · 236 comments497 words
github.com2025-04-14authorityHN front pageabout the DeepSeek familyHN 550 pts · 63 comments412 words

Community1

twitter.com2026-09-10HN front pageabout the DeepSeek familyHN 1,016 pts · 574 comments
All coverage tracked for this model (9) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
DeepSeek v4.1 Flashtwitter.com2026-09-102026-10-14hn
DeepSeek V4 Flash 0731 Intelligence, Performance and Price Analysisartificialanalysis.ai2026-07-312026-10-14hn
DeepSeek OCRgithub.com2025-10-202026-10-14hn
The path to open-sourcing the DeepSeek inference enginegithub.com2025-04-142026-10-14hn
DeepSeek Open Infra: Open-Sourcing 5 AI Repos in 5 Daysgithub.com2025-02-212026-10-14hn
OpenAI says it has evidence DeepSeek used its model to train competitorft.com2025-01-292026-10-14hn
DeepSeek releases Janus Pro, a text-to-image generator [pdf]github.com2025-01-272026-10-14hn
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RLarxiv.org2025-01-252026-10-14hn
DeepSeek-R1github.com2025-01-202026-10-14hn

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 · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-14 14:07 UTC · Last verified 2026-10-25

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