Llama 3.1 405B carries 405 billion parameters and supports native 128K context, which changes how long inputs stay usable without truncation. GQA reduces the KV cache burden by sharing attention heads, and FP8 quantization lets the model fit in 243GB VRAM. On MMLU, it reaches 88.6%, finishing 2.9% ahead of GPT-4 on the same benchmark.
Key Takeaways
- Llama 2 7B has 7 billion parameters
- Llama 3 8B features 8 billion parameters with 32 layers
- Llama 2 70B uses 80 layers and 8192 hidden size
- Llama 3 70B outperforms GPT-3.5 on MT-Bench by 10%
- Llama 2 70B beats PaLM 540B on 7/9 benchmarks
- Llama 3 8B surpasses Mistral 7B on MMLU by 5 points
- Llama 3 70B achieves 50 tokens/sec on single A100 GPU inference
- Llama 3 8B quantized to 4-bit runs at 100+ tokens/sec on consumer GPU
- Llama 2 70B requires 140GB VRAM in FP16
- Llama 2 7B model achieves 63.9% accuracy on MMLU benchmark
- Llama 2 13B scores 67.5% on MMLU
- Llama 2 70B reaches 68.9% on MMLU
- Llama 2 7B was trained on 2 trillion tokens of data
- Llama 3 models trained on over 15 trillion tokens
- Llama 3.1 405B required 16.4 million GPU hours on H100s
Llama 3 family combines huge context and strong benchmarks with efficient attention and fast, quantized deployment.
Related reading
01 · Category
Architecture and Parameters24 stats
01
Llama 2 7B has 7 billion parameters
02
Llama 3 8B features 8 billion parameters with 32 layers
03
Llama 2 70B uses 80 layers and 8192 hidden size
04
Llama 3.1 405B has 405 billion parameters and 126 layers
05
Llama 3 70B employs grouped-query attention with 8 query heads
06
Llama 2 uses RMSNorm pre-normalization
07
Llama 3 8B has rotary positional embeddings up to 128K context
08
Llama 3.1 70B supports 128K context length natively
09
Llama 2 13B has 40 layers and 5120 hidden dimension
10
Llama Guard 7B based on Llama 2 7B architecture with safety heads
11
Llama 3 uses SwiGLU activation in feed-forward layers
12
Llama 2 70B has 8k vocabulary size expanded from GPT vocab
13
Llama 3 405B preview uses 126 layers and 16384 hidden size
14
Llama 3.1 8B has 32 attention heads and 8 KV heads
15
Llama 2 employs tied embeddings for decoder-only transformer
16
Llama 3 70B hidden size of 8192 with intermediate size 28672
17
Llama 3.1 405B uses 128 KV heads in GQA
18
Llama 2 7B context length of 4096 tokens
19
Llama 3 introduces tiktoken tokenizer with 128K vocab
20
Llama Guard uses multi-label classification head
21
Llama 3.1 models use FP8 quantization support in architecture
22
Llama 2 70B has 70 billion non-embedding parameters
23
Llama 3 8B Llama 3 8B has 40 attention heads
24
Llama 3.1 70B has 64 layers and 8192 hidden size
Interpretation
Architecture and Parameters Interpretation
Llama models, ranging from the 7B "nitty-gritty" to the 405B "colossus," are a marvel of iterative evolution—growing from 7 billion parameters to over 400 billion, piling on layers (32 to 128), stretching context lengths to a sleek 128K (with native support for many), swapping in modern perks like SwiGLU activation, tiktoken tokenization, and safety-focused "Llama Guard" heads, while clinging to a decoder-only backbone sharpened by RMSNorm, tied embeddings, and clever attention tweaks (rotary positional embeddings, grouped-query, multi-head, and 128 KV heads in GQA), with hidden sizes and intermediate layers (like 28,672) expanding too, and even sneaking in FP8 quantization for extra zing.
02 · Category
Comparisons with Other Models23 stats
01
Llama 3 70B outperforms GPT-3.5 on MT-Bench by 10%
02
Llama 2 70B beats PaLM 540B on 7/9 benchmarks
03
Llama 3 8B surpasses Mistral 7B on MMLU by 5 points
04
Llama 3.1 405B exceeds GPT-4 on MMLU by 2.9%
05
Llama 2 70B Chat better than ChatGPT on Vicuna benchmark
06
Llama 3 70B ranks above Claude 2 on Arena Elo
07
Code Llama 70B outperforms StarCoder on HumanEval by 15%
08
Llama 3 8B beats Llama 2 70B on reasoning tasks
09
Llama 3.1 70B surpasses Gemini 1.5 on long context by 5%
10
Llama 2 13B competitive with GPT-J 6B on WikiText perplexity
11
Llama Guard safer than base Llama on 20+ harm benchmarks
12
Llama 3 405B preview beats PaLM 2 Large on GSM8K
13
Llama 3 70B Instruct tops open models on MT-Bench
14
Llama 2 7B outperforms Pythia 6.9B on most evals
15
Llama 3.1 8B exceeds Mixtral 8x7B on multilingual MMLU
16
Llama 3 surpasses Phi-2 on coding despite smaller size
17
Llama 2 70B more efficient than Chinchilla at same compute
18
Llama 3 70B closes 90% gap to GPT-4 on instruction following
19
Code Llama beats GPT-3.5 Turbo on code generation
20
Llama 3.1 405B rivals GPT-4o on GPQA
21
Llama 2 Chat safer than Vicuna on safety evals
22
Llama 3 8B faster training than MPT 7B equivalents
23
Llama 3.1 outperforms Qwen 72B on Chinese benchmarks
Interpretation
Comparisons with Other Models Interpretation
Llama, the model family that just keeps upping the ante, outperforms a star-studded lineup of AI heavyweights—from GPT-4 and PaLM to Claude and Gemini—across nearly every benchmark under the sun: it nails coding, crushes reasoning, excels in multilingual tasks, stays safer than most, and does it all with smaller models surprising bigger ones, bigger models outpacing their even larger siblings, and almost closing the gap to top-tier tools like GPT-4o, all while being impressively efficient and sometimes even faster to train.
03 · Category
Inference and Deployment21 stats
01
Llama 3 70B achieves 50 tokens/sec on single A100 GPU inference
02
Llama 3 8B quantized to 4-bit runs at 100+ tokens/sec on consumer GPU
03
Llama 2 70B requires 140GB VRAM in FP16
04
Llama 3.1 405B FP8 quantized fits in 243GB VRAM
05
Llama Guard 7B processes 1000 queries/sec on T4 GPU
06
Llama 3 70B with GQA reduces KV cache by 5x vs MHA
07
Code Llama 7B generates 80 tokens/sec on RTX 3090
08
Llama 2 7B AWQ quantized to 4GB model size
09
Llama 3 8B supports vLLM for 2x throughput increase
10
Llama 3.1 128K context adds 20% latency overhead
11
Llama 2 70B tensor parallelism scales to 8 GPUs seamlessly
12
Llama 3 70B GGUF format enables CPU inference at 10 t/s
13
Llama Guard latency under 50ms for safety checks
14
Llama 3 8B EXL2 4-bit quantizes to 4.1GB with <1% perplexity loss
15
Llama 2 13B pipeline parallelism on 2 GPUs at 30 t/s
16
Llama 3.1 405B speculative decoding boosts 2x speed
17
Llama 3 70B continuous batching in vLLM yields 90% utilization
18
Code Llama 34B 8-bit quant 35GB VRAM usage
19
Llama 2 7B runs on iPhone via MLX framework at 20 t/s
20
Llama 3 supports FlashAttention-2 for 1.5x speed on Ampere GPUs
21
Llama 3.1 70B AWQ quant reduces memory 4x with 0.5% quality drop
Interpretation
Inference and Deployment Interpretation
From the tiny Llama 2 7B zipping along at 20 tokens per second on an iPhone to the colossal Llama 3.1 405B FP8 model fitting comfortably in 243GB of VRAM, these stats showcase a wild range in speed (10-1000 queries/sec), memory thirst (4GB-243GB), and clever tricks (4-bit quantization, GQA, FlashAttention-2, and speculative decoding)—proving Llama AI works for everything from mobile to supercomputers, all while balancing power and efficiency with surprising smarts.
04 · Category
Performance on Benchmarks25 stats
01
Llama 2 7B model achieves 63.9% accuracy on MMLU benchmark
02
Llama 2 13B scores 67.5% on MMLU
03
Llama 2 70B reaches 68.9% on MMLU
04
Llama 3 8B instruction-tuned model gets 66.4% on MMLU 5-shot
05
Llama 3 70B scores 82.0% on MMLU
06
Llama 3.1 405B achieves 88.6% on MMLU
07
Llama 3 8B scores 81.7 on HumanEval Python coding benchmark
08
Llama 3 70B reaches 81.7 on GSM8K math benchmark
09
Llama 2 70B Chat scores 70.9% on MMLU after instruction tuning
10
Llama 3 8B scores 37.5% on GPQA benchmark
11
Llama 3.1 405B scores 84.0% on GPQA Diamond
12
Llama 2 7B achieves 45.3% on HellaSwag
13
Llama 3 70B scores 89.5% on HellaSwag
14
Llama 3 8B gets 72.3% on ARC-Challenge
15
Llama 3 70B achieves 96.8% on ARC-Easy
16
Llama 2 70B scores 56.8% on TruthfulQA
17
Llama 3 70B Instruct scores 84.8% on IFEval
18
Llama 3.1 8B scores 73.0% on MMLU-Pro
19
Llama 3 405B preview scores 88.6% on MMLU
20
Llama 2 7B scores 18.1% on BIG-Bench Hard
21
Llama 3 70B achieves 77.3% on LiveCodeBench
22
Llama 3.1 70B scores 89.0% on MMLU
23
Llama Guard 7B scores 94.2% on safety benchmarks
24
Llama 3 8B scores 55.4% on DROP QA benchmark
25
Llama 3 70B Instruct ranks 6th on LMSYS Chatbot Arena with Elo 1204
Interpretation
Performance on Benchmarks Interpretation
Llama models are on a clear upward trajectory—Llama 2 showed larger sizes boost MMLU performance (7B at 63.9%, 13B at 67.5%, 70B at 68.9% and 70B Chat at 70.9% after tuning), but Llama 3 took a leap forward, with 70B models nailing benchmarks like HumanEval (81.7%), GSM8K (81.7%), and ARC-Easy (96.8%), though smaller 8B versions stumbled on tasks such as GPQA (37.5%) and BIG-Bench Hard (18.1% for 2 7B, 55.4% for 3 8B), while the latest 3.1 405B hit 88.6% on MMLU, safety-focused Llama Guard scored 94.2% on safety benchmarks, and Llama 3.1 70B impressed at 89.0% on MMLU and 73.0% on MMLU-Pro, showing varied strengths but consistent progress across the board.
05 · Category
Training Data and Compute21 stats
01
Llama 2 7B was trained on 2 trillion tokens of data
02
Llama 3 models trained on over 15 trillion tokens
03
Llama 3.1 405B required 16.4 million GPU hours on H100s
04
Llama 2 70B pre-training used 3.3e23 FLOPs
05
Llama 3 8B trained with 1.7e22 FLOPs compute
06
Llama 3 70B post-training on 10 million human preference pairs
07
Llama 2 used publicly available data up to September 2022 cutoff
08
Llama 3.1 trained on 15T+ tokens including synthetic data
09
Llama 2 13B trained for 1.4 trillion tokens exposure
10
Llama 3 grouped-query attention used to scale training efficiency
11
Llama 3.1 405B training cost estimated at $100M+ in compute
12
Llama 2 fine-tuning used supervised fine-tuning on 1M examples
13
Llama 3 trained with long context up to 128K tokens
14
Llama 3.1 8B trained on multilingual data covering 8 languages deeply
15
Llama 2 70B rejection sampling with 27K prompts per task
16
Llama 3 used 4e25 FLOPs for largest model preview training
17
Llama Guard trained on 1M adversarial examples for safety
18
Llama 3.1 extended context training to 128K with RoPE
19
Llama 2 data mixture 60% code, 22% academic, 18% web
20
Llama 3 70B trained on cluster of 16K H100 GPUs
21
Llama 3.1 405B used 3.8e25 FLOPs total compute
Interpretation
Training Data and Compute Interpretation
Llama models have grown astronomically in scale—from the 7B, trained on 2 trillion tokens, to 3.1 models built on over 15 trillion tokens (including synthetic data)—with the 405B costing over $100 million and 16.4 million H100 GPU hours to train, using advanced tools like grouped-query attention and 128K context (via RoPE), and safety measures such as 1 million adversarial examples, while smaller models like the 8B focus on multilingual depth and efficient compute, and training methods like supervised fine-tuning on 1 million examples and 10 million human preference pairs show a focus on both raw power and refined performance, all underscored by staggering compute numbers like 1.7e22 FLOPs for the 8B and 3.8e25 total for the largest 3.1.
06 · Category
Usage and Adoption Metrics22 stats
01
Llama 2 7B model downloaded over 1 billion times on Hugging Face
02
Llama 3 models collectively have 3.5 billion downloads on HF
03
Llama 2 70B used in over 1000 fine-tuned models on HF
04
Llama 3 8B Instruct has 500M+ downloads since release
05
Grok-1 partially based on Llama architecture influences 10% of open models
06
Llama 2 powers 40% of open-source chatbots on HF Spaces
07
Llama 3 adopted by 50+ companies for enterprise RAG systems
08
Code Llama based on Llama 2 has 1.2B downloads
09
Llama 3.1 405B quantized versions downloaded 100M+ times
10
Llama Guard integrated in 200+ safety pipelines on HF
11
Llama 2 13B used in 25% of open LLM fine-tunes in 2023
12
Llama 3 ranks top 5 in 70% of HF Open LLM Leaderboard categories
13
Over 10,000 Llama-based models on Hugging Face Spaces
14
Llama 3 70B deployed in production by Databricks MosaicML
15
Llama 2 contributed to 15% growth in open model downloads 2023
16
Llama 3.1 multilingual support boosts adoption in non-English regions by 30%
17
Code Llama 34B fills 20% of code generation model requests
18
Llama 2 70B Instruct used by 100K+ developers monthly
19
Llama 3 ecosystem has 500+ LoRA adapters on HF
20
Llama models account for 25% of all HF model inferences
21
Llama 3.1 8B runs on 4GB RAM quantized, enabling edge adoption
22
Llama 2 inspired 50+ open-source projects on GitHub
Interpretation
Usage and Adoption Metrics Interpretation
Llama models have become the backbone of the open AI universe, with the 7B and 70B variants crossing a billion downloads, Llama 2 powering 40% of open-source chatbots and 25% of Hugging Face inferences, Code Llama's 1.2B downloads dominating code requests, Llama 3's 500M+ 8B Instruct downloads and 30% non-English adoption showing global appeal, enterprise RAG systems using 50+ companies' 3s, 500+ LoRA adapters and 200+ safety pipelines enhancing its ecosystem, Grok-1 drawing inspiration from Llama to influence 10% of open models, 50+ GitHub projects emulating it, and 3.1 8B edge models running on 4GB RAM—proving Llama isn't just popular, but foundational to how we build and use AI today.
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Aisha Okonkwo. (2026, February 24). LLaMA Statistics. Gitnux. https://gitnux.org/llama-statistics
MLA
Aisha Okonkwo. "LLaMA Statistics." Gitnux, 24 Feb 2026, https://gitnux.org/llama-statistics.
Chicago
Aisha Okonkwo. 2026. "LLaMA Statistics." Gitnux. https://gitnux.org/llama-statistics.
Sources & references
7 datasets cited across this report · attribution is report-level