Llama-3.1-70B-Instruct

meta-llama Llama 3.1 Community License out 2 years ago

70.6B parameters, text in, text out. 1 provider serves it.

Verified 2026-10-25 Rank #124 in LLMs · score 95.1 method v1 Sources 3 Downloads 30d 89.5k · #325 of 619 tracked

Can I run it?42GB at INT4Estimatefits a 64 GB Mac (unified memory)method

Can I use it?Llama 3.1 Community License'Built with Llama' attribution; >700M MAU clauselicence file2026-10-24

Do people use it?89.5kdownloads, 30 days#325 of 619 we track · below the median+3 likes, downloads +8% this week1,012 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 · 395 HN pts

Show HN: Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPU

ntransformer is a C++/CUDA inference engine that runs Llama 70B on a single RTX 3090 using a 3-tier adaptive caching system with VRAM-resident layers, pinned RAM, and NVMe/mmap fallback.

Disabling IOMMU removes hardware DMA isolation between all PCIe devices, so do not run on multi-tenant or server systems.
The NVMe direct backend writes the GGUF model file to raw NVMe blocks via dd before inference begins.

github.com 2026-02-21 395 HN pts · 101 comments 1.6k words

Field report

g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

g1 is an experimental prototype using prompting strategies to improve Llama-3.1-70b reasoning capabilities through o1-like chains.

The system requires the LLM to respond in JSON format with 'title', 'content', and 'next_action' keys.
The prompt explicitly instructs the model to use at least 3 methods to derive the answer.

github.com 2024-09-15 334 HN pts · 148 comments 1.1k words

Latest · 16 months ago

Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book

understandingai.org 2025-06-15 191 HN pts · 305 comments about the Llama 3.1 family family

25 best of 35 tracked

The 3 picks above, plus these 22. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

ArticleLlama 3.1llama.meta.com · 2024-07-23 · 437 HN pts · official
ArticleIntroducing Llama 3.1: Our most capable models to dateai.meta.com · undated · official
ArticleLlama 3.1 405B now runs at 969 tokens/s on Cerebras Inferencecerebras.ai · 2024-11-19
CodeLlama 3.1 Omni Modelgithub.com · 2024-09-18
CodeShow HN: Tune LLaMa3.1 on Google Cloud TPUsgithub.com · 2024-09-11 · 189 HN pts
CodeLlama 3.1 in Cgithub.com · 2024-07-24 · 212 HN pts
CodeLongwriter – Increase llama3.1 output to 10k wordsgithub.com · 2024-10-07 · 154 HN pts
ArticleHermes 3: The First Fine-Tuned Llama 3.1 405B Modellambdalabs.com · 2024-08-15
ArticleLlama 3.1: Our most capable models to dateai.meta.com · 2024-07-23 · 27 HN pts · official
DiscussionCerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8Btwitter.com · 2024-08-27 · 95 HN pts
ArticleLlama 3.1 Instruct 70B Intelligence, Performance & Price ...artificialanalysis.ai · undated
ArticleWhy Llama 3.1 is Importanttimkellogg.me · 2024-07-23
ArticleNvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8Bresearch.nvidia.com · 2024-11-17
ArticleMeta Llama 3.1 405Bopenrouter.ai · 2024-07-23
ArticleCoding with Llama 3.1, New DeepSeek Coder and Mistral Largeaider.chat · 2024-07-25
ArticleLlama-3.1 supports tool calls via promptingbraintrust.dev · 2024-07-26
ArticleMeta releases an open-weights GPT-4-level AI model, Llama 3.1 405Barstechnica.com · 2024-07-23
ArticleShow HN: Run Llama 3.1 8B in the browserapp.wiz.chat · 2024-07-28
VideoRunning the latest Llama 3.1 8B on Raspberry Pi [video]youtube.com · 2024-07-30
ArticleShow HN: Finetune Llama-3.1 2x faster in a Colabcolab.research.google.com · 2024-07-23
ArticleBenchmarking Llama 3.1 405B on 8x AMD MI300X GPUsdstack.ai · 2024-10-09
DiscussionReflection-Llama-3.1-70B is Llama-3 with LoRAold.reddit.com · 2024-09-08

Show all 22

Where to run it

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

#	Cheapest live row	In /1M	Out /1M	Context	Quant	Seen
1	Featherless	—	—	—	—	Oct 25

All 1 provider →

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 1 API provider; heavily run locally (127 quantized builds); a big base for builders (236 fine-tunes, adapters and merges).

As a service1Featherless
On own hardware127quantized builds
Built on236109 fine-tunes · 105 adapters · 22 merges
In apps100+Hugging Face Spaces

Top Spaces running it: KingNish/OpenGPT-4o ♥ 4,302 · gokaygokay/FLUX-Prompt-Generator ♥ 1,400 · pliny-the-prompter/obliteratus ♥ 621 · allenai/reward-bench ♥ 438

Will it fit?

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

Fits a 64 GB Mac (unified memory) at INT4

About 42 GB at INT4, 85 GB at FP8, 169 GB at BF16: weights only, so leave room for the KV cache.

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

The story so far

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

9 dated articles and 0 repo commits so far

Teal is the weights (2024-07-16), amber triangles are 9 dated articles (each links to it).

What people built on it

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

363 derivatives on the Hub, led by 127 quantized

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

GGUF MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF for llama.cpp, Ollama, LM Studio 12.1M downloads MLX mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED for Apple silicon 550k downloads FP8 RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8 for vLLM, SGLang 2M downloads

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.

Chat and follow instructionsstructuraldecision-model estimate p=0.95 name: Instruct tag: conversational

Facts

17 fields

Listed in LLMs

Facts

Llama-3.1-70B-Instruct has 70.6B parameters. (safetensors metadata, observed 2026-10-24)
License: Llama 3.1 Community License (friction: attribution). 'Built with Llama' attribution; >700M MAU clause. (license file, observed 2026-10-24)
Created 2024-07-16, last modified 2024-12-15. (HF API, observed 2026-10-24)
Access is gated on Hugging Face (manual). (HF API, observed 2026-10-24)

Pipeline tag	text-generation
Library	transformers
Architectures	LlamaForCausalLM
Model type	llama
Params by dtype	BF16: 70,553,706,496
Size band	XL
MoE	no
Gated	manual
Papers	arXiv:2204.05149
License	Llama 3.1 Community License (llama3.1)
Hub snapshot	2026-10-25

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-3.1-70B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)	169 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)	85 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)	42 GB · Single high-end GPU (<80 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-3.1-70B (same organization) Family: meta-llama/Llama-3.1 363 derivatives on the Hub

Base model: meta-llama/Llama-3.1-70B

quantized: 127finetune: 109adapter: 105merge: 22

Top variants (40 shown, ranked by downloads)

Variant	Relation	Quant	Params	Band	License	Author	Likes	Downloads
MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	—	MaziyarPanahi	41	12.1M
RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8	quantized	-FP8	70.6B	XL	llama3.1	RedHatAI	52	2M
RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w4a16	quantized	-quant	70.6B	XL	llama3.1	RedHatAI	33	1.9M
mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED	finetune	-bf16	70.6B	XL	llama3.1	mlx-community	0	550k
bartowski/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	llama3.1	bartowski	77	538.7k
reissbaker/llama-3.1-70b-abliterated-lora	adapter	—	—	—	—	reissbaker	4	339.2k
nvidia/Llama-3.1-70B-Instruct-FP8	finetune	-FP8	70.6B	XL	llama3.1	nvidia	21	263k
unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit	quantized	-bnb	70.6B	XL	llama3.1	unsloth	32	261.3k
unsloth/Meta-Llama-3.1-70B-Instruct	quantized	—	70.6B	XL	llama3.1	unsloth	4	243.6k
RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w8a8	quantized	-quant	70.6B	XL	llama3.1	RedHatAI	21	234.2k
amd/Llama-3.1-70B-Instruct-FP8-KV	quantized	-FP8	70.6B	XL	other	amd	5	177.6k
mattshumer/Reflection-Llama-3.1-70B	finetune	—	70.6B	XL	llama3.1	mattshumer	1.7k	55.3k
lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	llama3.1	lmstudio-community	39	132.2k
RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8-dynamic	quantized	-FP8	70.6B	XL	llama3.1	RedHatAI	7	102.8k
nvidia/Llama-3.1-Nemotron-70B-Instruct	finetune	—	—	—	llama3.1	nvidia	570	43.9k
nvidia/Llama-3.1-Nemotron-70B-Reward-HF	finetune	—	70.6B	XL	llama3.1	nvidia	93	59.8k
shenzhi-wang/Llama3.1-70B-Chinese-Chat	quantized	GGUF	70.6B	XL	llama3.1	shenzhi-wang	45	56k
legraphista/Meta-Llama-3.1-70B-Instruct-IMat-GGUF	quantized	GGUF	—	—	llama3.1	legraphista	11	55.1k
mlabonne/Llama-3.1-70B-Instruct-lorablated	finetune	—	70.6B	XL	llama3.1	mlabonne	80	50.7k
infly/INF-ORM-Llama3.1-70B	finetune	—	69.6B	XL	—	infly	27	43.7k
mlabonne/Llama-3.1-70B-Instruct-lorablated-GGUF	quantized	GGUF	—	—	llama3.1	mlabonne	48	31.7k
mradermacher/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	llama3.1	mradermacher	3	33.4k
mradermacher/Meta-Llama-3.1-70B-Instruct-i1-GGUF	quantized	GGUF	—	—	llama3.1	mradermacher	2	27k
EPFLiGHT/Meditron3-70B	finetune	—	70.6B	XL	llama3.1	EPFLiGHT	25	25k
qwp4w3hyb/Meta-Llama-3.1-70B-Instruct-iMat-GGUF	quantized	GGUF	—	—	llama3.1	qwp4w3hyb	8	24.2k
unsloth/Reflection-Llama-3.1-70B	finetune	—	70.6B	XL	llama3.1	unsloth	8	20.8k
MaziyarPanahi/calme-2.2-llama3.1-70b	finetune	—	70.6B	XL	—	MaziyarPanahi	2	18.5k
second-state/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	llama3.1	second-state	1	17.5k
RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w8a16	quantized	-quant	70.6B	XL	llama3.1	RedHatAI	5	15.1k
MaziyarPanahi/calme-2.3-llama3.1-70b	finetune	—	70.6B	XL	—	MaziyarPanahi	3	13.4k
Skywork/Skywork-Critic-Llama-3.1-70B	finetune	—	—	—	other	Skywork	12	12.8k
HiTZ/Latxa-Llama-3.1-70B-Instruct	finetune	—	70.6B	XL	other	HiTZ	7	12.3k
c4tdr0ut/grok-oss-Revenant-70B	quantized	GGUF	70.6B	XL	other	c4tdr0ut	5	11.8k
backyardai/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	llama3.1	backyardai	3	11.3k
ISTA-DASLab/Meta-Llama-3.1-70B-Instruct-AQLM-PV-2Bit-1x16	quantized	—	11.0B	L	—	ISTA-DASLab	45	8.1k
nvidia/Llama-3.1-Nemotron-70B-Reward	finetune	—	—	—	llama3.1	nvidia	83	4.8k
RedHatAI/Llama-3.1-70B-Instruct-NVFP4	quantized	NVFP4	40.6B	XL	llama3.1	RedHatAI	1	8.3k
SicariusSicariiStuff/Assistant_Pepe_70B	finetune	—	70.6B	XL	llama3.1	SicariusSicariiStuff	80	3.9k
gaianet/Meta-Llama-3.1-70B-Instruct-GGUF	quantized	GGUF	—	—	llama3.1	gaianet	0	7.4k
reissbaker/r1-llama-70b-distill-lora	adapter	—	—	—	apache-2.0	reissbaker	0	7.1k

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for Llama-3.1-70B-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.

Provider	Model ref	Context	Quant	$/M in	$/M out	tok/s	TTFT	Tools	Status	Observed
Featherless hf	meta-llama/Llama-3.1-70B-Instruct	—	—	—	—	—	—	—	live	2026-10-25

Articles and coverage

35 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.

Official3

Llama 3.1

llama.meta.com2024-07-23officialHN front pageabout the Llama 3.1 familyHN 437 pts · 269 comments42 words

Introducing Llama 3.1: Our most capable models to date

ai.meta.comofficialauthorityabout the Llama 3.1 family454 words

Meta released Muse Wonder on OCT 12, 2026, as a frontier model leading on personal and professional agents. Muse Spark 1.3 delivers improved performance across agentic and coding tasks, while Muse Image is the most advanced image generation model yet. The company also announced a partnership with EssilorLuxottica to launch Meta Glasses on June 23, 2026.written, reviewed, cited

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

Analysis6

Show HN: Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPU

github.com2026-02-21authorityHN front pageHN 395 pts · 101 comments1,595 words

ntransformer is a C++/CUDA inference engine that runs Llama 70B on a single RTX 3090 using a 3-tier adaptive caching system with VRAM-resident layers, pinned RAM, and NVMe/mmap fallback. The engine achieves an 83x speedup over mmap baseline for 70B on consumer hardware and requires zero external dependencies beyond the CUDA Toolkit. It supports GGUF quantization formats including Q4_K_M and Q6_K under a BSD-2-Clause license.written, reviewed, cited

Disabling IOMMU removes hardware DMA isolation between all PCIe devices, so do not run on multi-tenant or server systems.
The NVMe direct backend writes the GGUF model file to raw NVMe blocks via dd before inference begins.
Layer skip calibration uses cosine similarity to eliminate 20 out of 80 layers per token with minimal quality loss.

g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

github.com2024-09-15authorityHN front pageHN 334 pts · 148 comments1,148 words

g1 is an experimental prototype using prompting strategies to improve Llama-3.1-70b reasoning capabilities through o1-like chains. Without prompting, Llama-3.1-70b had 0% accuracy on the Strawberry problem, but g1 achieves ~70% accuracy on this task. Initial testing shows the system accurately solves simple logic problems 60-80% of the time that usually stump LLMs.written, reviewed, cited

The system requires the LLM to respond in JSON format with 'title', 'content', and 'next_action' keys.
The prompt explicitly instructs the model to use at least 3 methods to derive the answer.

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

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

Title	Source	Published	First seen	Signals
Introducing Llama 3.1: Our most capable models to date	ai.meta.com official	—	2026-10-07	exa
Llama 3.1 Instruct 70B Intelligence, Performance & Price ...	artificialanalysis.ai	—	2026-10-07	exa
LLM Deep-dive: Llama 3.1	predli.com	—	2026-10-07	exa
Llama 3.1 70B Instruct Benchmarks, Pricing & Context Window	llm-stats.com	—	2026-10-07	exa
Llama 3.1 70B Instruct	catalog.ngc.nvidia.com	—	2026-10-07	exa
Evaluation: Llama 3.1 70B vs. Comparable Closed-Source ...	vellum.ai	—	2026-10-07	exa
Meta: Llama 3.1 70B Instruct - AI Model Details & Benchmarks	benchable.ai	—	2026-10-07	exa
Show HN: Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPU	github.com	2026-02-21	2026-10-07	hn
Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book	understandingai.org	2025-06-15	2026-10-07	hn
Nvidia's new Llama-3.1 Nemotron Ultra outperforms DeepSeek R1 at half the size	venturebeat.com	2025-04-11	2026-10-07	hn
Llama 3.1 405B now runs at 969 tokens/s on Cerebras Inference	cerebras.ai	2024-11-19	2026-10-07	hn
Nvidia presents Llama-Mesh: Generating 3D Mesh with Llama 3.1 8B	research.nvidia.com	2024-11-17	2026-10-07	hn
Nvidia Llama-3.1-Nemotron-70B-Instruct	build.nvidia.com	2024-10-16	2026-10-07	hn
Benchmarking Llama 3.1 405B on 8x AMD MI300X GPUs	dstack.ai	2024-10-09	2026-10-07	hn
Longwriter – Increase llama3.1 output to 10k words	github.com	2024-10-07	2026-10-07	hn
Llama 3.1 Omni Model	github.com	2024-09-18	2026-10-07	hn
g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains	github.com	2024-09-15	2026-10-07	hn
Show HN: Tune LLaMa3.1 on Google Cloud TPUs	github.com	2024-09-11	2026-10-07	hn
Reflection-Llama-3.1-70B is Llama-3 with LoRA	old.reddit.com	2024-09-08	2026-10-07	hn
Cerebras launches inference for Llama 3.1; benchmarked at 1846 tokens/s on 8B	twitter.com	2024-08-27	2026-10-07	hn
Hermes 3: The First Fine-Tuned Llama 3.1 405B Model	lambdalabs.com	2024-08-15	2026-10-07	hn
Running the latest Llama 3.1 8B on Raspberry Pi [video]	youtube.com	2024-07-30	2026-10-07	hn
Show HN: Run Llama 3.1 8B in the browser	app.wiz.chat	2024-07-28	2026-10-07	hn
Llama-3.1 supports tool calls via prompting	braintrust.dev	2024-07-26	2026-10-07	hn
Coding with Llama 3.1, New DeepSeek Coder and Mistral Large	aider.chat	2024-07-25	2026-10-07	hn
Llama 3.1 in C	github.com	2024-07-24	2026-10-07	hn
Why Llama 3.1 is Important	timkellogg.me	2024-07-23	2026-10-07	hn
Show HN: Finetune Llama-3.1 2x faster in a Colab	colab.research.google.com	2024-07-23	2026-10-07	hn
Meta releases an open-weights GPT-4-level AI model, Llama 3.1 405B	arstechnica.com	2024-07-23	2026-10-07	hn
Meta Llama 3.1 405B	openrouter.ai	2024-07-23	2026-10-07	hn
Chat with Meta Llama 3.1 405B	llama3.replicate.dev	2024-07-23	2026-10-07	hn
Llama 3.1: Our most capable models to date	ai.meta.com official	2024-07-23	2026-10-07	hn
Llama 3.1	llama.meta.com official	2024-07-23	2026-10-07	hn
Llama 3.1 70B Instruct – 128k context, open weights \| LLM Reference	llmreference.com	2024-07-23	2026-10-07	exa
Llama 3.1 70B Review — Benchmarks, Pricing & AI Panel Verdict \| TopReviewed.ai	topreviewed.ai	2024-07-22	2026-10-07	exa

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).

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink cited in card

David Patterson, Joseph Gonzalez, Urs Hölzle +7 · 2022-04-11 · PDF · HF paper page (13 upvotes)

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:34 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:01 UTC · Last verified 2026-10-25

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