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Models › Llama-3.1 family

Llama-3.1-8B-Instruct

Hugging Face ↗ Follow changes

meta-llama Llama 3.1 Community License out 2 years ago

8.03B parameters, text in, text out, 128K tokens of context. 4 providers serve it from $0.020 per million input tokens.

Verified 2026-10-25 Rank #11 in LLMs · score 129.4 method v1 Sources 3 Downloads 30d 4.9M · #32 of 619 tracked

Changed this week: 13 new quantized builds on the Hub (935 in all).2026-10-24 · 12 new quantized builds on the Hub (934 in all).2026-10-24 · 1 new provider: Nscale.2026-10-23

Can I run it?4.8GB at INT4Estimatefits a 16 GB cardmethod
What does it cost?$0.020/1M incheapest of 3DeepInfra2026-10-25
Can I use it?Llama 3.1 Community License'Built with Llama' attribution; >700M MAU clauselicence file2026-10-24
Does it work?25.83on BFCL#23 of 28 measuredleaderboard2026-10-25
Do people use it?4.9Mdownloads, 30 days#32 of 619 we track · top 5%+115 likes, downloads −14% this week8,585 likesHugging Face2026-10-24

Worth reading

Picked by rule from 35 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 212 HN pts

Llama 3.1 in C

github.com 2024-07-24 212 HN pts · 36 comments 5.9k words about the Llama 3.1 family family

25 best of 35 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. CodeShow HN: Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPUgithub.com · 2026-02-21
  2. ArticleLlama 3.1llama.meta.com · 2024-07-23 · 437 HN pts · official
  3. Codeg1: Using Llama-3.1 70B on Groq to create o1-like reasoning chainsgithub.com · 2024-09-15
  4. ArticleLlama 3.1 405B now runs at 969 tokens/s on Cerebras Inferencecerebras.ai · 2024-11-19
  5. CodeLlama 3.1 Omni Modelgithub.com · 2024-09-18
  6. CodeShow HN: Tune LLaMa3.1 on Google Cloud TPUsgithub.com · 2024-09-11 · 189 HN pts
  7. CodeLongwriter – Increase llama3.1 output to 10k wordsgithub.com · 2024-10-07 · 154 HN pts
  8. ArticleHermes 3: The First Fine-Tuned Llama 3.1 405B Modellambdalabs.com · 2024-08-15
  9. ArticleLlama 3.1: Our most capable models to dateai.meta.com · 2024-07-23 · 27 HN pts · official
  10. DiscussionCerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8Btwitter.com · 2024-08-27 · 95 HN pts
  11. ArticleWhy Llama 3.1 is Importanttimkellogg.me · 2024-07-23 · 56 HN pts
  12. ArticleNvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8Bresearch.nvidia.com · 2024-11-17 · 20 HN pts
  13. ArticleMeta Llama 3.1 405Bopenrouter.ai · 2024-07-23
  14. ArticleCoding with Llama 3.1, New DeepSeek Coder and Mistral Largeaider.chat · 2024-07-25 · 32 HN pts
  15. ArticleLlama-3.1 supports tool calls via promptingbraintrust.dev · 2024-07-26
  16. ArticleMeta releases an open-weights GPT-4-level AI model, Llama 3.1 405Barstechnica.com · 2024-07-23
  17. ArticleShow HN: Run Llama 3.1 8B in the browserapp.wiz.chat · 2024-07-28
  18. VideoRunning the latest Llama 3.1 8B on Raspberry Pi [video]youtube.com · 2024-07-30
  19. ArticleShow HN: Finetune Llama-3.1 2x faster in a Colabcolab.research.google.com · 2024-07-23
  20. ArticleBenchmarking Llama 3.1 405B on 8x AMD MI300X GPUsdstack.ai · 2024-10-09
  21. CodeShow HN: Dive into Transformers and LLM World – Llama 3.1 in Go, Step by Stepgithub.com · 2024-08-18
  22. DiscussionReflection-Llama-3.1-70B is Llama-3 with LoRAold.reddit.com · 2024-09-08
  23. ArticleNvidia's new Llama-3.1 Nemotron Ultra outperforms DeepSeek R1 at half the sizeventurebeat.com · 2025-04-11

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 3×

3 providers with a live price, from $0.020 to $0.060 per million input tokens; the median is $0.020. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00Novita: $0.020 in / $0.050 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMappingDeepInfra: $0.020 in / $0.050 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMappingNscale: $0.060 in / $0.060 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMappingcheapest $0.020median $0.020
$0.010$0.10$1.00$10.00Novita: $0.020 in / $0.050 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMappingDeepInfra: $0.020 in / $0.050 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMappingNscale: $0.060 in / $0.060 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct?expand[]=inferenceProviderMappingcheapest $0.020 · median $0.020 (white tick)
#Cheapest 4In /1MOut /1MContextQuantSeen
1 DeepInfra $0.020 $0.050 128K — Oct 25
2 Novita $0.020 $0.050 16K — Oct 25
3 Nscale $0.060 $0.060 128K — Oct 25
4 Featherless — — — — Oct 25

All 4 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 4 API providers; heavily run locally (935 quantized builds); a big base for builders (6,362 fine-tunes, adapters and merges).

  • As a service4DeepInfra, Featherless, Novita, Nscale
  • On own hardware935quantized builds
  • Built on6,3623,285 fine-tunes · 2,972 adapters · 105 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: KingNish/OpenGPT-4o ♥ 4,302 · pliny-the-prompter/obliteratus ♥ 621 · allenai/reward-bench ♥ 438 · cvachet/pdf-chatbot ♥ 382

Will it fit?

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

Fits a 16 GB card at INT4

About 4.8 GB at INT4, 9.6 GB at FP8, 19 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 4.8 GB of weights (estimate; KV cache and activations extra)INT44.8 GBFP8: about 9.6 GB of weights (estimate; KV cache and activations extra)FP89.6 GBBF16: about 19 GB of weights (estimate; KV cache and activations extra)BF1619 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 4.8 GB of weights (estimate; KV cache and activations extra)INT44.8 GBFP8: about 9.6 GB of weights (estimate; KV cache and activations extra)FP89.6 GBBF16: about 19 GB of weights (estimate; KV cache and activations extra)BF1619 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.

#23 of 28 on BFCL so far

25.83. Each tick is a model on this site with a measured BFCL row; author-reported numbers stay in Reference. Percent (overall accuracy). Prompt

zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmoonshotai/Kimi-K2-Instruct: 59.06 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmldeepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 52.15 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 47.99 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlSalesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmicrosoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-1.7B: 28.41 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html010025.83 · #23 of 28
zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmoonshotai/Kimi-K2-Instruct: 59.06 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmldeepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 52.15 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 47.99 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlSalesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmicrosoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-1.7B: 28.41 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html010025.83 · #23 of 28

Source · leaderboard · 2026-10-25

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 (2024-07-18), amber triangles are 10 dated articles (each links to it).

AugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctLlama 3.1 · 2024-07-23Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book · 2025-06-15Show HN: Tune LLaMa3.1 on Google Cloud TPUs · 2024-09-11Llama 3.1 in C · 2024-07-24Longwriter – Increase llama3.1 output to 10k words · 2024-10-07Llama 3.1: Our most capable models to date · 2024-07-23Cerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8B · 2024-08-27Why Llama 3.1 is Important · 2024-07-23Nvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8B · 2024-11-17Coding with Llama 3.1, New DeepSeek Coder and Mistral Large · 2024-07-25Weights 2024-07-18 · 2024-07-18Weights 2024-07-18now · 4 providers
AugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctLlama 3.1 · 2024-07-23Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book · 2025-06-15Show HN: Tune LLaMa3.1 on Google Cloud TPUs · 2024-09-11Llama 3.1 in C · 2024-07-24Longwriter – Increase llama3.1 output to 10k words · 2024-10-07Llama 3.1: Our most capable models to date · 2024-07-23Cerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8B · 2024-08-27Why Llama 3.1 is Important · 2024-07-23Nvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8B · 2024-11-17Coding with Llama 3.1, New DeepSeek Coder and Mistral Large · 2024-07-25Weights 2024-07-18 · 2024-07-18Weights 2024-07-18now · 4 providers

What people built on it

7,297 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

7,297 derivatives on the Hub, led by 3,285 fine-tunes

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

935 quantized (Hugging Face model tree)3,285 fine-tunes (Hugging Face model tree)105 merges (Hugging Face model tree)2,972 adapters (Hugging Face model tree)935 quantized3,285 fine-tunes105 merges2,972 adapters
935 quantized (Hugging Face model tree)3,285 fine-tunes (Hugging Face model tree)105 merges (Hugging Face model tree)2,972 adapters (Hugging Face model tree)935 quantized3,285 fine-tunes105 merges2,972 adapters
GGUF modularai/Llama-3.1-8B-Instruct-GGUF for llama.cpp, Ollama, LM Studio 19.7M downloads MLX mlx-community/Llama-3.1-8B-Instruct-4bit for Apple silicon 1.1M downloads FP8 RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 for vLLM, SGLang 7.1M downloads

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 BFCL · Call tools and APIs25.83 percent (overall accuracy)Prompt
5 sub-scores
  • Non-Live AST Acc: 84
  • Live Acc: 70.76
  • Multi Turn Acc: 11.12
  • Web Search Acc: 3
  • Memory Acc: 10.75
leaderboard2026-10-25

Use-case evidence

3 use cases

What it is good at 3 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

19 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesLlamaForCausalLM
Model typellama
Params by dtypeBF16: 8,030,261,248
Size bandM
MoEno
Context length131,072 (provider listing)
Gatedmanual
PapersarXiv:2204.05149
LicenseLlama 3.1 Community License (llama3.1)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · LlamaForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · LlamaForCausalLM · 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-3.1-8B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)19 GB · Single GPU (<24 GB)
FP8 (1 byte/param)9.6 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)4.8 GB · Single GPU (<24 GB)

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

All variants

40 variants

Lineage

Fine-tuned from meta-llama/Llama-3.1-8B (same organization) Family: meta-llama/Llama-3.1 7,297 derivatives on the Hub

Base model: meta-llama/Llama-3.1-8B

quantized: 935finetune: 3,285adapter: 2,972merge: 105

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
modularai/Llama-3.1-8B-Instruct-GGUF quantized GGUF — — llama3 modularai 17 19.7M
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF quantized GGUF — — llama3.1 bartowski 426 14.9M
MaziyarPanahi/Meta-Llama-3.1-8B-Instruct-GGUF quantized GGUF — — llama3.1 MaziyarPanahi 40 12.1M
unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit quantized -bnb 8.25B M llama3.1 unsloth 107 9.7M
RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 quantized -FP8 8.03B M llama3.1 RedHatAI 45 7.1M
unsloth/Llama-3.1-8B-Instruct finetune — 8.03B M llama3.1 unsloth 17 2.8M
nvidia/Llama-3.1-8B-Instruct-FP8 finetune -FP8 8.03B M llama3.1 nvidia 43 2.5M
lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF quantized GGUF — — llama3.1 lmstudio-community 264 2.2M
SanctumAI/Meta-Llama-3.1-8B-Instruct-GGUF quantized GGUF — — llama3.1 SanctumAI 18 2.1M
unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit quantized -bnb 8.24B M llama3.1 unsloth 4 1.8M
RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 quantized -quant 8.03B M llama3.1 RedHatAI 30 1.7M
Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 finetune — 7.50B M — Skywork 43 1.4M
nvidia/Llama-3.1-8B-Instruct-NVFP4 quantized NVFP4 4.54B M other nvidia 18 1.4M
mlx-community/Llama-3.1-8B-Instruct-4bit quantized -4bit 8.03B M llama3.1 mlx-community 11 1.1M
westlake-repl/Evolla-10B-hf finetune — 10.4B L mit westlake-repl 1 1.1M
Team-ACE/ToolACE-8B finetune — 8.03B M apache-2.0 Team-ACE 77 921.2k
mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated finetune — 8.03B M llama3.1 mlabonne 217 881.4k
RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic quantized -FP8 8.03B M llama3.1 RedHatAI 9 853.8k
yejingfu/nmagic-Meta-Llama-3.1-8B-Instruct-FP8 quantized -FP8 8.03B M llama3.1 yejingfu 0 840.8k
unsloth/meta-Llama-3.1-8B-unsloth-bnb-4bit quantized -bnb 8.24B M llama3.1 unsloth 1 834.1k
unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit quantized -bnb 8.24B M llama3.1 unsloth 4 812.8k
watt-ai/watt-tool-8B finetune — 8.03B M apache-2.0 watt-ai 119 739.2k
amd/Llama-3.1-8B-Instruct-FP8-KV quantized -FP8 8.03B M other amd 6 636.1k
mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF quantized GGUF — — llama3.1 mlabonne 242 508.9k
shenzhi-wang/Llama3.1-8B-Chinese-Chat quantized GGUF 8.03B M llama3.1 shenzhi-wang 266 497.2k
RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 quantized -quant 8.03B M llama3.1 RedHatAI 20 492.5k
Skywork/Skywork-Reward-V2-Llama-3.1-8B finetune — 7.50B M llama3.1 Skywork 50 343.2k
zai-org/cogvlm2-llama3-caption finetune — 12.5B L other zai-org 119 309.8k
unsloth/Llama-3.1-8B-Instruct-bnb-4bit quantized -bnb 8.25B M llama3.1 unsloth 1 267.5k
GaleneAI/llama-3.1-nemoguard-8b-content-safety-merged-NVFP4 quantized NVFP4 4.98B M llama3.1 GaleneAI 0 250.2k
pearl-ai/Llama-3.1-8B-Instruct-pearl quantized — 8.03B M llama3.1 pearl-ai 6 248.3k
Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3 finetune — 8.03B M apache-2.0 Kukedlc 3 237.7k
unsloth/Llama-3.1-8B-Instruct-GGUF quantized GGUF — — llama3.1 unsloth 49 227.2k
Vikhrmodels/Vikhr-Llama3.1-8B-Instruct-R-21-09-24 finetune — 8.03B M apache-2.0 Vikhrmodels 35 221.7k
GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF quantized GGUF — — llama3.1 GPT4All-Community 12 196.9k
RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3 finetune — 950M S llama3.1 RedHatAI 3 195.5k
RedHatAI/Llama-3.1-8B-Instruct-NVFP4 quantized NVFP4 4.98B M llama3.1 RedHatAI 2 169.5k
WillHeld/DiVA-llama-3-v0-8b finetune — 2.49B M mpl-2.0 WillHeld 35 160k
DeepMount00/Llama-3.1-8b-ITA finetune — 8.03B M — DeepMount00 17 152k
nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3 finetune — 8.03B M other nvidia 29 134.6k

Repository changelog

3 changes
changed in the last 7 daysNew providers: 2026-10-23Other: 2026-10-24, 2026-10-24all changes

All providers

4 providers · 4 endpoints

Where to run 4 providers · 4 endpoints

Hosted endpoints observed for Llama-3.1-8B-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: $0.02/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Novita
hf
 meta-llama/llama-3.1-8b-instruct 16K — $0.02 $0.05 151.7 575 ms no live 2026-10-25
DeepInfra
hf
 meta-llama/Meta-Llama-3.1-8B-Instruct 128K — $0.02 $0.05 42.7 269 ms yes live 2026-10-25
Nscale
hf
 meta-llama/Llama-3.1-8B-Instruct 128K — $0.06 $0.06 133.8 437 ms no live 2026-10-25
Featherless
hf
 meta-llama/Llama-3.1-8B-Instruct — — — — — — — live 2026-10-25

Articles and coverage

35 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency.

Official2

Llama 3.1
llama.meta.com2024-07-23officialHN front pageabout the Llama 3.1 familyHN 437 pts · 269 comments42 words
Llama 3.1: Our most capable models to date
ai.meta.com2024-07-23officialauthorityabout the Llama 3.1 familyHN 27 pts · 3 comments2,136 words

Analysis7

Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book
understandingai.org2025-06-15HN front pageabout the Llama 3.1 familyHN 191 pts · 305 comments
Show HN: Tune LLaMa3.1 on Google Cloud TPUs
github.com2024-09-11authorityHN front pageabout the Llama 3.1 familyHN 189 pts · 52 comments869 words
Llama 3.1 in C
github.com2024-07-24authorityHN front pageabout the Llama 3.1 familyHN 212 pts · 36 comments5,858 words
Longwriter – Increase llama3.1 output to 10k words
github.com2024-10-07authorityHN front pageabout the Llama 3.1 familyHN 154 pts · 29 comments6,696 words
Why Llama 3.1 is Important
timkellogg.me2024-07-23about the Llama 3.1 familyHN 56 pts · 23 comments
Nvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8B
research.nvidia.com2024-11-17authorityHN 20 pts · 1 comments
Coding with Llama 3.1, New DeepSeek Coder and Mistral Large
aider.chat2024-07-25about the Llama 3.1 familyHN 32 pts · 7 comments

Community1

Cerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8B
twitter.com2024-08-27about the Llama 3.1 familyHN 95 pts · 42 comments
All coverage tracked for this model (35) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Llama 3.1 8B Instruct – 128k context, open weights | LLM Referencellmreference.com—2026-10-07exa
Llama3.1-8B-Instruct: Architecture & Adaptationemergentmind.com—2026-10-07exa
Llama 3.1 8B Instruct Benchmarks, Pricing & Specs — Vector Wirevectorwire.ai—2026-10-07exa
Llama-3.1-8B-Instruct (Meta) Benchmarks, Context Window, Architecture & API Pricing (2026) | TheModelverse | TheModelversethemodelverse.in—2026-10-07exa
Meta: Llama 3.1 8B Instruct by Meta AI - AI Model Details | LLMIndex | LLMIndexllmindex.net—2026-10-07exa
LLM Deep-dive: Llama 3.1predli.com—2026-10-07exa
Meta: Llama 3.1 8B Instruct Review | Pricing, Benchmarks & Capabilities (2026) | Design for Onlinedesignforonline.com2026-02-252026-10-07exa
Show HN: Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPUgithub.com2026-02-212026-10-07hn
Meta's Llama 3.1 can recall 42 percent of the first Harry Potter bookunderstandingai.org2025-06-152026-10-07hn
Nvidia's new Llama-3.1 Nemotron Ultra outperforms DeepSeek R1 at half the sizeventurebeat.com2025-04-112026-10-07hn
Llama 3.1 405B now runs at 969 tokens/s on Cerebras Inferencecerebras.ai2024-11-192026-10-07hn
Nvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8Bresearch.nvidia.com2024-11-172026-10-07hn
Benchmarking Llama 3.1 405B on 8x AMD MI300X GPUsdstack.ai2024-10-092026-10-07hn
Longwriter – Increase llama3.1 output to 10k wordsgithub.com2024-10-072026-10-07hn
Llama 3.1 Omni Modelgithub.com2024-09-182026-10-07hn
g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chainsgithub.com2024-09-152026-10-07hn
Show HN: Tune LLaMa3.1 on Google Cloud TPUsgithub.com2024-09-112026-10-07hn
Reflection-Llama-3.1-70B is Llama-3 with LoRAold.reddit.com2024-09-082026-10-07hn
Cerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8Btwitter.com2024-08-272026-10-07hn
Show HN: Dive into Transformers and LLM World – Llama 3.1 in Go, Step by Stepgithub.com2024-08-182026-10-07hn
Hermes 3: The First Fine-Tuned Llama 3.1 405B Modellambdalabs.com2024-08-152026-10-07hn
Running the latest Llama 3.1 8B on Raspberry Pi [video]youtube.com2024-07-302026-10-07hn
Show HN: Run Llama 3.1 8B in the browserapp.wiz.chat2024-07-282026-10-07hn
Llama-3.1 supports tool calls via promptingbraintrust.dev2024-07-262026-10-07hn
Coding with Llama 3.1, New DeepSeek Coder and Mistral Largeaider.chat2024-07-252026-10-07hn
Llama 3.1 in Cgithub.com2024-07-242026-10-07hn
Why Llama 3.1 is Importanttimkellogg.me2024-07-232026-10-07hn
Show HN: Finetune Llama-3.1 2x faster in a Colabcolab.research.google.com2024-07-232026-10-07hn
Meta releases an open-weights GPT-4-level AI model, Llama 3.1 405Barstechnica.com2024-07-232026-10-07hn
Meta Llama 3.1 405Bopenrouter.ai2024-07-232026-10-07hn
Chat with Meta Llama 3.1 405Bllama3.replicate.dev2024-07-232026-10-07hn
Llama 3.1: Our most capable models to dateai.meta.com official2024-07-232026-10-07hn
Llama 3.1llama.meta.com official2024-07-232026-10-07hn
Meta unleashes Llama 3.1 | UnfoldAIunfoldai.com2024-07-232026-10-07exa
Llama 3.1 8B Instruct Review: Specs, Pricing & Benchmarks | llmdb.appllmdb.app2024-07-232026-10-07exa

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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  • Meta-Llama-3-8B-Instruct · 8.03B
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  • Qwen2.5-7B-Instruct · 7.62B
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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:31 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 14:59 UTC · Last verified 2026-10-25

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