Llama 3.3 70B
Open-weight heavyweight model matching the performance of earlier 405B models with 128k context and multilingual support.
Llama 3.3 70B
Open-weight heavyweight model matching the performance of earlier 405B models with 128k context and multilingual support.
- • FP16 Weights = 70.0B × 2B = 140.00 GB
- • INT4 Weights = 70.0B × 0.55B = 38.50 GB
- • KV Cache (128000 ctx, FP16) ≈ 78.13 GB
- • Activation Buffer = ~20% overhead
Hardware & Execution ParametersLLM
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of Llama 3.3 70B by Meta, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
A dense architecture leveraging Grouped-Query Attention (GQA, 8:1 ratio) and 128k RoPE scaling, pre-trained on a massive 15T token corpus.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding.
LLM Hardware Sizing & Serving
Llama's dense GQA structure scales quadratically. 128k context demands PagedAttention and chunked prefill to prevent Out-of-Memory (OOM) during heavy batched decoding.
Inference Economics & Workflows
Well-suited for enterprise pipelines where capability is balanced against per-million token costs.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying Llama 3.3 70B.
Key Architectural Strengths
- Dense Grouped-Query Attention (GQA 8:1) and 128k RoPE scaling ensures robust multi-step coherence.
- Trained on a massive 15T+ token corpus, offering premier baseline intelligence for open weights.
- Massive 128,000-token context allows full-repository and book-length ingestion.
- Ultra cost-effective inference at $0/1M input tokens enables high-frequency agent loops.
Operational Considerations
- Dense attention footprint saturates memory bandwidth heavily during large-batch autoregressive decoding.
- 128k+ token prefill stages become heavily compute-bound and balloon KV cache without PagedAttention chunking.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for Llama 3.3 70B.
python3 -m vllm.entrypoints.openai.api_server --model Llama 3.3 70B --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run llama 3.3 70bpython3 -m sglang.launch_server --model-path Llama 3.3 70B --tp 4 --trust-remote-codetext-generation-launcher --model-id Llama 3.3 70B --num-shard 4 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~168.0 GB VRAM required
~84.0 GB VRAM (Hopper speedup)
~46.2 GB VRAM
CPU RAM / Apple Silicon optimized
LLM Benchmark Database & Performance Metrics
4 TestedStandardized evaluation results across reasoning, agentic coding, computer use, and alignment.
MMLU
HumanEval
MATH
GPQA
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Usage Tier | Rate / Unit |
|---|---|
| Prompt / Input Tokens | $0 / 1M tokens |
| Completion / Output Tokens | $0 / 1M tokens |
Comparable Foundation Architectures
Alternative models in the LLM class with similar capabilities, context windows, or deployment profiles.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("META_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="llama-3-3-70b",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about Llama 3.3 70B
Essential facts, architectural specs, hardware constraints, and pricing answers for Llama 3.3 70B.
To run Llama 3.3 70B (70B Dense) locally, you generally need 40-80 GB VRAM (4-bit to 8-bit). We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for Llama 3.3 70B are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.