Models › Kimi-K3 family

RadixArk Not declared out 3 months ago

2.25B parameters, text in, text out, 1M tokens of context.

Verified 2026-10-25 Rank #616 in LLMs · score 73.7 method v1 Sources 3 Downloads 30d 3.5M · #42 of 619 tracked

Can I run it?1.3GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Not declaredNo license declared on the model cardlicence file2026-10-24
Do people use it?3.5Mdownloads, 30 days#42 of 619 we track · top 7%+5 likes, downloads −27% this week78 likesHugging Face2026-10-24

Worth reading

Picked by rule from 29 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 2,107 HN pts

Kimi K3: Open Frontier Intelligence

Kimi K3 is a 2.8T-parameter open model featuring native vision and a 1-million-token context window.

kimi.com 2026-07-16 2,107 HN pts · 1,214 comments 2.6k words about the Kimi K3 family family
Latest · 47 days ago

Kimi K3 (2.8T) at 1 token/s on a MacBook Pro, streamed from four SSDs

A fork of Deltafin runs the full 2.8-trillion-parameter Kimi K3 model on a single M5 Max MacBook Pro with 128 GB RAM, streaming 1.45 TB of expert weights from four SSDs.

  • The slowest read among a layer's 16 experts sets the pace, not total bandwidth.
  • Streaming setup initially runs far more slowly when routes have no local cache yet.
github.com 2026-09-08 279 HN pts · 156 comments 2.1k words about the Kimi K3 family family

25 best of 29 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. ArticleKimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTAfireworks.ai · 2026-07-21 · 877 HN pts
  2. ArticleKimi K3-256kkimi.com · 2026-07-29
  3. ArticleKimi K3 Architecture Overview and Notessebastianraschka.com · 2026-07-28 · 507 HN pts
  4. ArticleKimi K3, and what we can still learn from the pelican benchmarksimonwillison.net · 2026-07-17 · 407 HN pts
  5. CodeRun Kimi K3 using 29 GB of RAM at 0.50 tok/sgithub.com · 2026-07-31 · 341 HN pts
  6. CodeKimi-K3 Technical Report [pdf]github.com · 2026-07-27 · 391 HN pts
  7. ArticleThe Kimi K3 Momentstephen.bochinski.dev · 2026-07-18 · 636 HN pts
  8. ArticleKimi K3, Qwen 3.8, and Anthropic's (Potential) Unravellingemergingtrajectories.com · 2026-07-20 · 371 HN pts
  9. CodeRunning Kimi K3 on a M1 Maxgithub.com · 2026-07-28 · 138 HN pts
  10. ArticleRunning Kimi K3 on MI355X at Better Performance per Dollar Than B300wafer.ai · 2026-08-02
  11. DiscussionKimi K3 exploited the latest Redis servertwitter.com · 2026-07-23
  12. DiscussionMoonshot AI suspends new subscriptions due to Kimi K3 demandtwitter.com · 2026-07-19
  13. ArticleKimi K3 Now Available via Telnyx Inference APItelnyx.com · 2026-07-27
  14. ArticleSelf-hosting Kimi K3: 20% more hardware cost, 20% better task resolutionaistack.imec-int.com · 2026-07-29
  15. ArticleKimi K3: second only to Fable 5 on AA-Briefcaseartificialanalysis.ai · 2026-07-22
  16. ArticleUK AISI / Caisi Preliminary Assessment of Kimi K3's Cyber Capabilitiesnist.gov · 2026-07-25
  17. ArticleKimi K3 built a Windows XP in browserwindows-xp.kimi.site · 2026-07-25
  18. ArticleKimi K3 Intelligence, Performance and Price Analysisartificialanalysis.ai · 2026-07-16
  19. ArticleKimi K3: The open-weights escalationinterconnects.ai · 2026-07-20
  20. DiscussionKimi K3 is now #1 in the Front end Code Arena with 1679 pts, surpassing Fable 5twitter.com · 2026-07-16
  21. ArticleKimi K3's Design Secret May Be in Its Thinking Tracesnotes.designarena.ai · 2026-07-30
  22. ArticleLLM City – 3D render of all Kimi K3's weights as 2.5mm tilesmagik.net · 2026-08-17
  23. ArticleKimi K3 is not cheapalexinch.com · 2026-07-26

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: 2 quantized builds and 2 Spaces.

  • As a service0no API provider listed
  • On own hardware2quantized builds
  • Built on00 fine-tunes · 0 adapters · 0 merges
  • In apps2Hugging Face Spaces

Top Spaces running it: embedl/hfviewer ♥ 181

Will it fit?

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

Fits a 16 GB card at INT4

About 1.3 GB at INT4, 2.7 GB at FP8, 5.4 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 1.3 GB of weights (estimate; KV cache and activations extra)INT41.3 GBFP8: about 2.7 GB of weights (estimate; KV cache and activations extra)FP82.7 GBBF16: about 5.4 GB of weights (estimate; KV cache and activations extra)BF165.4 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 1.3 GB of weights (estimate; KV cache and activations extra)INT41.3 GBFP8: about 2.7 GB of weights (estimate; KV cache and activations extra)FP82.7 GBBF16: about 5.4 GB of weights (estimate; KV cache and activations extra)BF165.4 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.

Most coverage came in the first two weeks

Teal is the weights (2026-07-27), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

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

2 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.

2 quantized (Hugging Face model tree)2 quantized
2 quantized (Hugging Face model tree)2 quantized

All variants →

Reference

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

Benchmarks

13 rows

Benchmarks 13 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (13)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AIME 2026 · Reason through hard problems2.99—model author2026-10-07
author-reported AIME 2026 · Reason through hard problems3.130–1Kmodel author2026-10-07
author-reported AIME 2026 · Reason through hard problems2.581–2Kmodel author2026-10-07
author-reported AIME 2026 · Reason through hard problems2.562–4Kmodel author2026-10-07
author-reported AIME 2026 · Reason through hard problems2.724–8Kmodel author2026-10-07
author-reported AIME 2026 · Reason through hard problems4.9232K+model author2026-10-07
author-reported GSM8K · Reason through hard problems5.42—model author2026-10-07
author-reported HumanEval · Write code with agents5.51—model author2026-10-07
author-reported MATH-500 · Reason through hard problems4.13—model author2026-10-07
author-reported MBPP · Write code with agents5.2—model author2026-10-07
author-reported MT-Bench · Chat and follow instructions3.93—model author2026-10-07
author-reported RULER · Read long documents4.26—model author2026-10-07
author-reported SWE-Rebench4.66—model author2026-10-07

What the card says

3 cited sentences

Summary generated, cited

RadixArk/Kimi-K3-DSpark is a 2.25B parameter DSpark speculator designed for the Kimi K3 target model.[1][2] The model uses a DSparkDraftModel architecture to enable faster inference through speculative decoding.[3][4] It supports context lengths of up to 1 million tokens.[5]

  1. parameters human
  2. “A long-context DSpark speculator for Kimi K3”
  3. architectures
  4. “enabling faster inference through speculative decoding”
  5. “supports context lengths of up to 1 million tokens”

Generated 2026-10-07 15:15 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

6 use cases

What it is good at 6 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

20 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesDSparkDraftModel
Model typeqwen3
Dtypebfloat16
Params by dtypeBF16: 2,249,289,601
Size bandM
MoEno
Context length1,048,576 (config.json max_position_embeddings)
LicenseNot declared
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · DSparkDraftModel · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · DSparkDraftModel · 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 Kimi-K3-DSpark: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)5.4 GB · Single GPU (<24 GB)
FP8 (1 byte/param)2.7 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)1.3 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

2 variants

Lineage

Original model Family: RadixArk/Kimi-K3 2 derivatives on the Hub

quantized: 2

Top variants (2 shown, ranked by downloads)

Quant
2 rows
Variant Relation Quant Params Band License Author Likes Downloads
Lucebox/Kimi-K3-DSpark-Q8_0-GGUF quantized Q8_0 — — — Lucebox 1 2k
Anbeeld/Kimi-K3-DSpark-GGUF quantized GGUF — — — Anbeeld 0 1.1k

Repository changelog

6 commits

Repository changelog 6 commits

DateCommitSHA
2026-08-16 Link SGLang in the live-target-engine phrase 3c5bac3
2026-08-01 Update README.md 56ce616
2026-08-01 Update README.md f9d4737
2026-07-31 Sync Kimi-K3-DSpark-0731 snapshot and update README 9c4b257
2026-07-27 Add Kimi K3 DSpark model card eb03982
2026-07-27 Duplicate from cm00cm/DSpark-Kimi-K3-V1C 049288a

Articles and coverage

29 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.

Official1

kimi.com2026-07-16authorityHN front pageabout the Kimi K3 familyHN 2,107 pts · 1,214 comments2,558 words
Kimi K3 is a 2.8T-parameter open model featuring native vision and a 1-million-token context window. It utilizes Kimi Delta Attention and Attention Residuals alongside Stable LatentMoE, activating 16 out of 896 experts to achieve a 2.5x improvement in scaling efficiency. The model demonstrates frontier-level performance in long-horizon coding, chip design, and scientific research.written, reviewed, cited

Analysis9

github.com2026-09-08authorityHN front pageabout the Kimi K3 familyHN 279 pts · 156 comments2,117 words
A fork of Deltafin runs the full 2.8-trillion-parameter Kimi K3 model on a single M5 Max MacBook Pro with 128 GB RAM, streaming 1.45 TB of expert weights from four SSDs. One cold run with the exact prompt yields 1.00 tokens per second for 512-token answers and 0.96 tok/s on a public 17-token prompt, with per-run logs available in k3-public-bench/. A 512-token prompt takes approximately 6.3 minutes to generate the first token because prefill re-reads each layer's experts 8×.written, reviewed, cited
  • The slowest read among a layer's 16 experts sets the pace, not total bandwidth.
  • Streaming setup initially runs far more slowly when routes have no local cache yet.
  • The native server refuses anything it cannot honor exactly with a normal OpenAI-shaped error.
fireworks.ai2026-07-21authorityHN front pageabout the Kimi K3 familyHN 877 pts · 450 comments895 words
Fireworks benchmarks Kimi K3 against Fable 5 across 1,030 agentic tasks, finding that routing between the two achieves 93% accuracy. This approach is up to 50 times more cost effective than using Fable alone for long loops, with K3 handling symbolic math and Fable excelling in multi-language coding. An oracle router selects K3 for 72-96% of task traffic, leveraging its efficiency of roughly 55 turns and 1.3M tokens per SWE task versus Fable's 21 turns and 130K.written, reviewed, cited
  • K3 clears specific tasks Fable fails, including a 7z hash and FEAL cryptanalysis.
  • Tasks with extra turns generally mean more wall-clock time per run i.e. slower runs.
sebastianraschka.com2026-07-28authorityHN front pageabout the Kimi K3 familyHN 507 pts · 111 comments398 words
Kimi K3 is a 2.8T parameter open-weight model that replaces standard components with efficiency-tweaked versions like LatentMoE and multi-head latent attention. It introduces attention residuals to improve the residual path, adding about 4% in training cost and 2% in inference cost. The model removes all RoPE layers and uses NoPE everywhere, marking it as the first frontier-level architecture to do so.written, reviewed, cited
simonwillison.net2026-07-17authorityHN front pageabout the Kimi K3 familyHN 407 pts · 223 comments1,140 words
Moonshot AI announced Kimi K3, a 2.8 trillion parameter model available via API with open weights promised by July 27, 2026. The model costs $3/million input tokens and $15/million output tokens, consuming 13,241 reasoning tokens to generate a simple SVG for a total cost of 25 cents. While it surpasses Claude Opus 4.8 and GPT-5.5 high on benchmarks, it loses to Claude Fable 5 and GPT-5.6 Sol.written, reviewed, cited
  • Kimi K3's tokenizer counts the 'hi' prompt as 86 tokens, suggesting an 85 token hidden system prompt.
  • The model is the most expensive released by a Chinese AI lab to date at $3/million input tokens.
  • K3 currently only has one thinking effort level called 'max' rather than multiple options.
github.com2026-07-31authorityHN front pageabout the Kimi K3 familyHN 341 pts · 166 comments5,646 words
github.com2026-07-27authorityHN front pageabout the Kimi K3 familyHN 391 pts · 183 comments
stephen.bochinski.dev2026-07-18HN front pageabout the Kimi K3 familyHN 636 pts · 613 comments536 words
emergingtrajectories.com2026-07-20HN front pageabout the Kimi K3 familyHN 371 pts · 336 comments1,244 words
github.com2026-07-28authorityHN front pageabout the Kimi K3 familyHN 138 pts · 93 comments
All coverage tracked for this model (29) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Kimi K3 (2.8T) at 1 token/s on a MacBook Pro, streamed from four SSDsgithub.com2026-09-082026-10-07hn
LLM City – 3D render of all Kimi K3's weights as 2.5mm tilesmagik.net2026-08-172026-10-07hn
Running Kimi K3 on MI355X at Better Performance per Dollar Than B300wafer.ai2026-08-022026-10-07hn
Run Kimi K3 using 29 GB of RAM at 0.50 tok/sgithub.com2026-07-312026-10-07hn
Kimi K3's Design Secret May Be in Its Thinking Tracesnotes.designarena.ai2026-07-302026-10-07hn
Kimi K3-256kkimi.com2026-07-292026-10-07hn
Self-hosting Kimi K3: 20% more hardware cost, 20% better task resolutionaistack.imec-int.com2026-07-292026-10-07hn
Running Kimi K3 on a M1 Maxgithub.com2026-07-282026-10-07hn
Kimi K3 Architecture Overview and Notessebastianraschka.com2026-07-282026-10-07hn
Kimi K3 Now Available via Telnyx Inference APItelnyx.com2026-07-272026-10-07hn
Kimi-K3 Technical Report [pdf]github.com2026-07-272026-10-07hn
Kimi K3 is not cheapalexinch.com2026-07-262026-10-07hn
Kimi K3 after a week of real project use: what works, what doesn'tchenchen.guru2026-07-262026-10-07hn
Kimi K3 built a Windows XP in browserwindows-xp.kimi.site2026-07-252026-10-07hn
UK AISI / Caisi Preliminary Assessment of Kimi K3's Cyber Capabilitiesnist.gov2026-07-252026-10-07hn
Kimi K3 exploited the latest Redis servertwitter.com2026-07-232026-10-07hn
Kimi K3: second only to Fable 5 on AA-Briefcaseartificialanalysis.ai2026-07-222026-10-07hn
Kimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTAfireworks.ai2026-07-212026-10-07hn
Kimi K3 just fixed 15 critical security bugs that Codex and Fable refusedtwitter.com2026-07-202026-10-07hn
Kimi K3: The open-weights escalationinterconnects.ai2026-07-202026-10-07hn
Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravellingemergingtrajectories.com2026-07-202026-10-07hn
Tested Kimi K3 for Codingdotnetperls.com2026-07-202026-10-07hn
Moonshot AI suspends new subscriptions due to Kimi K3 demandtwitter.com2026-07-192026-10-07hn
The Kimi K3 Momentstephen.bochinski.dev2026-07-182026-10-07hn
Kimi K3 vs. Meta: Muse Spark 1.1runtimewire.com2026-07-182026-10-07hn
Kimi K3, and what we can still learn from the pelican benchmarksimonwillison.net2026-07-172026-10-07hn
Kimi K3 Intelligence, Performance and Price Analysisartificialanalysis.ai2026-07-162026-10-07hn
Kimi K3 is now #1 in the Front end Code Arena with 1679 pts, surpassing Fable 5twitter.com2026-07-162026-10-07hn
Kimi K3: Open Frontier Intelligencekimi.com2026-07-162026-10-07hn

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:42 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:15 UTC · Last verified 2026-10-25

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