Models › Llama-4 family

Llama-4-Maverick-17B-128E-Instruct

meta-llama Llama Community License (llama4) out 19 months ago

402B parameters, images and text in, text out, 512K tokens of context. 3 providers serve it from $0.19 per million input tokens.

Verified 2026-10-25 Rank #299 in LLMs · score 83.9 method v1 Sources 5 Downloads 30d 9.5k · #510 of 619 tracked

Changed this week: Context length changed: 1,048,576 → 524,288 tokens.2026-10-24

Can I run it?241GB at INT4Estimateneeds a multi-GPU nodemethod
What does it cost?$0.19/1M incheapest of 3DigitalOcean2026-10-24
Can I use it?Llama Community License (llama4)Attribution requiredlicence file2026-10-24
Does it work?15.6on Aider Polyglot#11 of 13 measuredleaderboard2025-04-06
Do people use it?9.5kdownloads, 30 days#510 of 619 we track · below the median+1 likes, downloads +2% this week517 likesHugging Face2026-10-24

Worth reading

Picked by rule from 28 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 109 HN pts
groq.com 2025-04-05 109 HN pts · 48 comments about the Llama 4 family family

25 best of 28 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. ArticleThe Llama 4 herdai.meta.com · 2025-04-05 · 1,235 HN pts · official
  2. ArticleMeta AI App built with Llama 4about.fb.com · 2025-04-29 · 98 HN pts · official
  3. ArticleThe Llama 4 Herdai.meta.com · 2025-04-05 · 48 HN pts · official
  4. ArticleCerebras achieves 2,500T/s on Llama 4 Maverick (400B)cerebras.ai · 2025-05-31 · 93 HN pts
  5. Articlegpt-oss-20b (High) vs Llama 4 Maverick: Model Comparisonartificialanalysis.ai · undated
  6. ArticleLlama 4 Smells Badfastml.com · 2025-04-24 · 41 HN pts
  7. ArticleLlama 4 Maverick on 8×H100: 3,714 tok/s peak, MT-Bench 9.03, and one shocking failure | InferenceBench Bloginferencebench.io · 2026-09-01
  8. ArticleYann LeCun confirms Meta's Llama 4 benchmarks were "fudged a little bit"tech.slashdot.org · 2026-01-05 · 30 HN pts
  9. ArticleReleased Llama 4 Maverick places 32nd in LMArenalmarena.ai · 2025-04-11
  10. ArticleLlama 4: Did Meta just push the panic button?interconnects.ai · 2025-04-07
  11. ArticleLlama 4 Is Banned in the EU: Open AI, Region-Lockeddionwiggins.substack.com · 2025-04-14
  12. DiscussionSerious issues in Llama 4 training. VP of AI at Meta resignedold.reddit.com · 2025-04-07
  13. ArticleLlama 4 performs worse than Llama 3 at translationnuenki.app · 2025-04-06
  14. ArticleMeta is using more than 100k Nvidia H100 AI GPUs to train Llama-4tomshardware.com · 2024-11-02
  15. ArticleMeta's surprise Llama 4 drop exposes the gap between AI ambition and realityarstechnica.com · 2025-04-07
  16. ArticleBias Reduced by 44% in Llama 4 Using Machine Unlearninghirundo.io · 2025-04-11
  17. ArticleLlama 4 underperforms: a benchmark against coding-centric modelsrootly.com · 2025-04-14
  18. ArticleMeta exec denies the company artificially boosted Llama 4's benchmark scorestechcrunch.com · 2025-04-07
  19. ArticleLlama 4 Guide: Scout, Maverick & Behemoth Status (2026)codersera.com · 2026-05-01
  20. ArticleMeta: Llama 4 Maverick - AI Model Details & Benchmarksbenchable.ai · undated
  21. ArticleLlama 4 Maverick (17Bx128E) | Open Laboratoryopenlaboratory.com · undated
  22. ArticleLlama 4 Maverick 17B 128E Instruct - Specs, Capabilities & Benchmarks | Inferbaseinferbase.ai · undated
  23. ArticleLlama 4 Maverick: 400B MoE Model — Self-Hosting and API Guideeffloow.hashnode.dev · undated

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

Prices for the same weights vary 2×

3 providers with a live price, from $0.19 to $0.35 per million input tokens; the median is $0.35. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00DigitalOcean: $0.19 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/meta-llama/llama-4-maverickParasail: $0.35 in / $1.00 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingGoogle: $0.35 in / $1.15 out per 1M · observed 2026-10-24 · https://openrouter.ai/meta-llama/llama-4-maverickcheapest $0.19median $0.35
$0.010$0.10$1.00$10.00DigitalOcean: $0.19 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/meta-llama/llama-4-maverickParasail: $0.35 in / $1.00 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingGoogle: $0.35 in / $1.15 out per 1M · observed 2026-10-24 · https://openrouter.ai/meta-llama/llama-4-maverickcheapest $0.19 · median $0.35 (white tick)
#Cheapest 3In /1MOut /1MContextQuantSeen
1 DigitalOcean $0.19 $0.65 125K unknown Oct 24
2 Parasail $0.35 $1.00 512K fp8 Oct 24
3 Google $0.35 $1.15 512K unknown Oct 24

All 3 providers →

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.

Served by 3 API providers; 17 quantized builds for local use.

  • As a service3DigitalOcean, Google, Parasail
  • On own hardware17quantized builds
  • Built on88 fine-tunes · 0 adapters · 0 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: pliny-the-prompter/obliteratus ♥ 621 · openfree/Llama-4-Maverick-17B-Research ♥ 88 · hadadrjt/ai ♥ 86 · whitecircle/circle-guard-bench ♥ 32

Will it fit?

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

Needs an 8×80 GB node even at INT4

About 241 GB at INT4, 482 GB at FP8, 964 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 241 GB of weights (estimate; KV cache and activations extra)INT4241 GBFP8: about 482 GB of weights (estimate; KV cache and activations extra)FP8482 GBBF16: about 964 GB of weights (estimate; KV cache and activations extra)BF16964 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 241 GB of weights (estimate; KV cache and activations extra)INT4241 GBFP8: about 482 GB of weights (estimate; KV cache and activations extra)FP8482 GBBF16: about 964 GB of weights (estimate; KV cache and activations extra)BF16964 GB

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

How it scores

Third-party measurements only; author-reported numbers stay in Reference, marked as such.

#11 of 13 on Aider Polyglot so far

15.6. Each tick is a model on this site with a measured Aider Polyglot row; author-reported numbers stay in Reference. Percent (pass_rate_2). edit_format=whole

deepseek-ai/DeepSeek-R1-0528: 71.4 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen3-235B-A22B: 59.6 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/moonshotai/Kimi-K2-Instruct: 59.1 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/deepseek-ai/DeepSeek-R1: 56.9 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/deepseek-ai/DeepSeek-V3-0324: 55.1 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/openai/gpt-oss-120b: 41.8 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen3-32B: 40 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/QwQ-32B: 26.2 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/QwQ-32B: 20.9 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen2.5-Coder-32B-Instruct: 16.4 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen2.5-Coder-32B-Instruct: 8 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/google/gemma-3-27b-it: 4.9 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/010015.6 · #11 of 13
deepseek-ai/DeepSeek-R1-0528: 71.4 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen3-235B-A22B: 59.6 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/moonshotai/Kimi-K2-Instruct: 59.1 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/deepseek-ai/DeepSeek-R1: 56.9 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/deepseek-ai/DeepSeek-V3-0324: 55.1 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/openai/gpt-oss-120b: 41.8 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen3-32B: 40 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/QwQ-32B: 26.2 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/QwQ-32B: 20.9 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen2.5-Coder-32B-Instruct: 16.4 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/Qwen/Qwen2.5-Coder-32B-Instruct: 8 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/google/gemma-3-27b-it: 4.9 · observed 2026-10-25 · https://aider.chat/docs/leaderboards/010015.6 · #11 of 13

Source · leaderboard · 2025-04-06

The story so far

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

What people built on it

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

25 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)8 fine-tunes (Hugging Face model tree)17 quantized8 fine-tunes
17 quantized (Hugging Face model tree)8 fine-tunes (Hugging Face model tree)17 quantized8 fine-tunes

All variants →

Reference

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

Benchmarks

1 row

Benchmarks 1 row

measured third-party leaderboard row (1)author-reported number from the model card or eval filesNo 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
measured Aider Polyglot · Write code with agents15.6 percent (pass_rate_2)edit_format=wholeleaderboard2025-04-06

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

18 fields

Listed in LLMs

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesLlama4ForConditionalGeneration
Model typellama4
Params by dtypeBF16: 401,583,781,376
Size bandXXL
MoEno
Context length524,288 (provider listing)
Gatedmanual
PapersarXiv:2204.05149
LicenseLlama Community License (llama4) (other)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Llama4ForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Llama4ForConditionalGeneration · 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 Llama-4-Maverick-17B-128E-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)964 GB · Cluster (≥640 GB)
FP8 (1 byte/param)482 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)241 GB · Multi-GPU node (<640 GB)

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

All variants

25 variants

Lineage

Fine-tuned from meta-llama/Llama-4-Maverick-17B-128E (same organization) Family: meta-llama/Llama-4 25 derivatives on the Hub

Base model: meta-llama/Llama-4-Maverick-17B-128E

quantized: 17finetune: 8

Top variants (25 shown, ranked by downloads)

Relation
Quant
Size
25 rows
Variant Relation Quant Params Band License Author Likes Downloads
unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF quantized GGUF — — other unsloth 52 335.5k
RedHatAI/Llama-4-Maverick-17B-128E-Instruct-FP8 quantized -FP8 402B XXL llama4 RedHatAI 2 59.9k
unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF-UD quantized GGUF — — other unsloth 3 47.5k
unsloth/Llama-4-Maverick-17B-128E-Instruct finetune — 402B XXL other unsloth 8 30.8k
RedHatAI/Llama-4-Maverick-17B-128E-Instruct-quantized.w4a16 quantized -quant 405B XXL other RedHatAI 1 29.8k
nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8 finetune -FP8 402B XXL other nvidia 15 16.3k
mlx-community/Llama-4-Maverick-17B-16E-Instruct-4bit quantized -4bit 401B XXL other mlx-community 7 13.7k
RedHatAI/Llama-4-Maverick-17B-128E-Instruct-NVFP4 quantized NVFP4 229B XXL llama3.1 RedHatAI 7 12.3k
kishizaki-sci/Llama-4-Maverick-17B-128E-Instruct-AWQ quantized AWQ 402B XXL llama4 kishizaki-sci 1 11.8k
unsloth/Llama-4-Maverick-17B-128E-Instruct-FP8 quantized -FP8 402B XXL other unsloth 6 9.5k
mlx-community/Llama-4-Maverick-17B-16E-Instruct-6bit quantized — 401B XXL other mlx-community 2 8.3k
GeorgyGUF/Llama-4-Maverick-17B-128E-Instruct-tq1_0.gguf quantized gguf — — llama4 GeorgyGUF 0 7.5k
DevQuasar-12/meta-llama.Llama-4-Maverick-17B-128E-Instruct-GGUF quantized GGUF — — — DevQuasar-12 0 6.7k
AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf quantized gguf — — other AaronimusPrime 1 3k
baa-ai/Llama-4-Maverick-17B-128E-Instruct-RAM-170GB-MLX quantized MLX 402B XXL other baa-ai 0 2.5k
RedHatAI/Llama-4-Maverick-17B-128E-Instruct finetune — 402B XXL llama4 RedHatAI 3 1k
noctrex/Llama-4-Maverick-17B-128E-Instruct-MXFP4_MOE-GGUF finetune GGUF — — — noctrex 0 1.1k
mradermacher/Llama-4-Maverick-17B-128E-Instruct-GGUF quantized GGUF — — other mradermacher 0 690
GeorgyGUF/Llama-4-Maverick-17B-128E-Instruct-bf16.gguf quantized -bf16 — — llama4 GeorgyGUF 0 483
GeorgyGUF/Llama-4-Maverick-17B-128E-Instruct-q8-with-bf16-embedding-and-bf16-output.gguf quantized -bf16 — — llama4 GeorgyGUF 0 454
RedHatAI/Llama-4-Maverick-17B-128E-Instruct-FP8-block quantized -FP8 402B XXL apache-2.0 RedHatAI 1 259
seanmamasde/llama4-maverick-17B-128E-eagle3-sglang finetune — 1.88B S mit seanmamasde 2 77
mradermacher/Llama-4-Maverick-17B-128E-Instruct-i1-GGUF finetune GGUF — — other mradermacher 2 0
Taylor658/Photonics_Distill_Llama_70B finetune — — — llama4 Taylor658 0 0
JSR-0003/Computer-Science_All-Courses-Guru finetune — — — llama4 JSR-0003 0 0

Repository changelog

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

All providers

3 providers · 3 endpoints

Where to run 3 providers · 3 endpoints

Hosted endpoints observed for Llama-4-Maverick-17B-128E-Instruct, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.19/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
DigitalOcean
openrouter
meta-llama/llama-4-maverick 125K unknown $0.19 $0.65 — — yes live 2026-10-24
Parasail
parasail, openrouter
parasail-llama-4-maverick-instruct-fp8 512K fp8 $0.35 $1.00 — — yes serverless 2026-10-24
Google
openrouter
meta-llama/llama-4-maverick 512K unknown $0.35 $1.15 — — yes live 2026-10-24

Articles and coverage

28 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.

Official4

ai.meta.com2025-04-05officialauthorityHN front pageabout the Llama 4 familyHN 1,235 pts · 658 comments3,404 words
Meta released Llama 4 Scout and Llama 4 Maverick, the first open-weight natively multimodal models using a mixture-of-experts (MoE) architecture. Llama 4 Scout features a 10M context window and fits on a single H100 GPU, while Llama 4 Maverick uses 128 experts to outperform GPT-4o on benchmarks. Both models are available for download today on llama.com and Hugging Face.written, reviewed, cited
  • Llama 4 Maverick achieves comparable results to DeepSeek v3 on reasoning and coding at less than half the active parameters.
  • The post-training pipeline removed more than 50% of data tagged as easy to prevent SFT and DPO from over-constraining the model.
  • Pre-training utilized FP8 precision on 32K GPUs to achieve 390 TFLOPs/GPU while maintaining high model FLOPs utilization.
about.fb.com2025-04-29officialabout the Llama 4 familyHN 98 pts · 109 comments1,036 words
Meta launches a standalone app for its Llama 4-powered AI assistant, featuring full-duplex speech technology that generates voice directly instead of reading written responses. Voice conversations, including the full duplex demo, are available in the US, Canada, Australia, and New Zealand to start. The web interface also gains an improved image generation experience and tests a rich document editor for generating documents full of text and images that can be exported as PDFs.written, reviewed, cited
  • The full-duplex voice demo lacks access to the web or real-time information.
  • Personalized responses are currently limited to the US and Canada only.
  • Users cannot start a conversation on the app and resume it on Ray-Ban Meta glasses.
ai.meta.com2025-04-05officialauthorityabout the Llama 4 familyHN 48 pts · 1 comments3,404 words
cerebras.ai2025-05-31about the Llama 4 familyHN 93 pts · 93 comments569 words
Cerebras Systems set a world record for Llama 4 Maverick inference speed, achieving 2,522 tokens per second according to Artificial Analysis. This performance exceeds NVIDIA Blackwell's 1,038 tokens per second and outpaces other vendors including SambaNova at 794 t/s, Amazon at 290 t/s, Groq at 549 t/s, Google at 125 t/s, and Microsoft Azure at 54 t/s. The Cerebras hardware and API are available now without requiring custom software optimizations.written, reviewed, cited

Analysis5

groq.com2025-04-05HN front pageabout the Llama 4 familyHN 109 pts · 48 comments
artificialanalysis.aiauthorityabout the Llama 4 family819 words
fastml.com2025-04-24about the Llama 4 familyHN 41 pts · 26 comments1,012 words
inferencebench.io2026-09-01about the Llama 4 family2,009 words
tech.slashdot.org2026-01-05about the Llama 4 familyHN 30 pts · 2 comments208 words

Community1

news.ycombinator.com2026-02-06about the Llama 4 familyHN 55 pts · 12 comments330 words
Meta's AI API has remained waitlist-only for 10 months since the Llama-4 release, prompting discussions on internal dysfunction. Commenters note that while Meta released updated segmentation models like SAM 3D objects, external users increasingly prefer DeepSeek. Speculation suggests the project may face derailing similar to previous Zuckerberg obsessions.written, reviewed, cited
All coverage tracked for this model (28) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
gpt-oss-20b (High) vs Llama 4 Maverick: Model Comparisonartificialanalysis.ai—2026-10-07exa
Meta: Llama 4 Maverick - AI Model Details & Benchmarksbenchable.ai—2026-10-07exa
Llama 4 Maverick (17Bx128E) | Open Laboratoryopenlaboratory.com—2026-10-07exa
Llama 4 Maverick 17B 128E Instruct - Specs, Capabilities & Benchmarks | Inferbaseinferbase.ai—2026-10-07exa
Llama 4 Maverick: 400B MoE Model — Self-Hosting and API Guideeffloow.hashnode.dev—2026-10-07exa
Llama 4 Maverick 17B 128E Instruct — Hardware Requirements & Compatibility | llmrunllmrun.dev—2026-10-07exa
Llama 4 Maverick on 8×H100: 3,714 tok/s peak, MT-Bench 9.03, and one shocking failure | InferenceBench Bloginferencebench.io2026-09-012026-10-07exa
Llama 4 Guide: Scout, Maverick & Behemoth Status (2026)codersera.com2026-05-012026-10-07exa
Llama 4 Maverick Review 2026: 400B MoE at $0.20-$0.50/M - TokenMix Blogtokenmix.ai2026-03-312026-10-07exa
Llama 4 Maverick | Awesome Agentsawesomeagents.ai2026-02-242026-10-07exa
Ask HN: 10 months since the Llama-4 release: what happened to Meta AI?news.ycombinator.com2026-02-062026-10-07hn
Yann LeCun confirms Meta's Llama 4 benchmarks were "fudged a little bit"tech.slashdot.org2026-01-052026-10-07hn
Cerebras achieves 2,500T/s on Llama 4 Maverick (400B)cerebras.ai2025-05-312026-10-07hn
Meta AI App built with Llama 4about.fb.com official2025-04-292026-10-07hn
Llama 4 Smells Badfastml.com2025-04-242026-10-07hn
Llama 4 underperforms: a benchmark against coding-centric modelsrootly.com2025-04-142026-10-07hn
Llama 4 Is Banned in the EU: Open AI, Region-Lockeddionwiggins.substack.com2025-04-142026-10-07hn
Released Llama 4 Maverick places 32nd in LMArenalmarena.ai2025-04-112026-10-07hn
Bias Reduced by 44% in Llama 4 Using Machine Unlearninghirundo.io2025-04-112026-10-07hn
Meta's surprise Llama 4 drop exposes the gap between AI ambition and realityarstechnica.com2025-04-072026-10-07hn
Meta exec denies the company artificially boosted Llama 4's benchmark scorestechcrunch.com2025-04-072026-10-07hn
Serious issues in Llama 4 training. VP of AI at Meta resignedold.reddit.com2025-04-072026-10-07hn
Llama 4: Did Meta just push the panic button?interconnects.ai2025-04-072026-10-07hn
Llama 4 performs worse than Llama 3 at translationnuenki.app2025-04-062026-10-07hn
Llama 4 Now Live on Groqgroq.com2025-04-052026-10-07hn
The Llama 4 Herdai.meta.com official2025-04-052026-10-07hn
The Llama 4 herdai.meta.com official2025-04-052026-10-07hn
Meta is using more than 100k Nvidia H100 AI GPUs to train Llama-4tomshardware.com2024-11-022026-10-07hn

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).

Similar models

3 models

Similar models

  • MiniMax-VL-01 · 456B
    same size band (XXL), same task (image-text-to-text)
  • DeepSeek-V4.1-Flash-1017 · 763B
    same size band (XXL), same task (image-text-to-text), more providers, permissive license
  • Qwen3.5-397B-A17B · 403B
    same size band (XXL), same task (image-text-to-text), more providers, permissive license

Sources

5 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:37 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:07 UTC · Last verified 2026-10-25

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