largeLlama 3.3 Community License70B paramsOpen weights

Llama 3.3 70B

70B parameter open-weight model. December 2024 Llama release with improved instruction following over 3.1 70B at the same 70B parameter count and 128K context. Q4 requires ~41 GB VRAM — suited for RTX 6000 Ada (48 GB), multi-GPU setups, or Mac with 64 GB+ unified memory.

Meta · Llama 3

Model overview

Llama 3.3 70B is Meta's refreshed 70B release, improving instruction-following over the earlier Llama 3.1 70B checkpoint while keeping the same parameter count and 128K context window. It covers a broad set of workflows well — chat, coding, RAG, agentic tool-use, and summarization all appear as tagged strengths — making it a versatile pick if you have the hardware to run a 70B-class model. Q4 quantization needs roughly 41 GB of VRAM, which fits a single 48 GB workstation card like the RTX 6000 Ada, a multi-GPU rig, or a Mac with 64 GB or more of unified memory. For most single consumer-GPU setups, a smaller Llama or Qwen checkpoint will be more practical.

Check your GPU fitWhat is VRAM?What is quantization?Local LLM quickstart

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesHuggingFace model card (meta-llama/Llama-3.3-70B-Instruct), official docs, OpenSourcesAI editorial review.

VRAM figures are empirical estimates. Actual usage varies by runtime, context length, and system configuration. Verify on your specific hardware before production use.

Will Llama 3.3 70B run on your machine?

Llama 3.3 70B is 70B parameters and needs 42.5 GB of VRAM at Q4_K_M — 41 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_M41 GB42.5 GBgood
Q8_072 GB73.5 GBhigh
FP16142 GB143.5 GBreference

Fit on common hardware at Q4_K_M

HardwareMemory the model can useSystem RAMVerdict
CPU OnlyNone (CPU only)16 GBToo large
RTX 4060 Laptop8 GB16 GBToo large
RTX 3060 (12GB)12 GB32 GBToo large
RTX 4060 Ti (16GB)16 GB32 GBToo large
RTX 309024 GB64 GBToo large
Apple Silicon (Unified Memory) 36 GB27 GB of 36 GB36 GBToo large
RTX 509032 GB64 GBToo large
Apple Silicon (Unified Memory) 48 GB36 GB of 48 GB48 GBToo large
Apple Silicon (Unified Memory) 64 GB48 GB of 64 GB64 GBComfortable
Apple Silicon (Unified Memory) 96 GB72 GB of 96 GB96 GBComfortable
Apple Silicon (Unified Memory) 128 GB96 GB of 128 GB128 GBComfortable

Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.

Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.

Check Llama 3.3 70B against your exact hardwareHow these verdicts are calculated

Need more hardware for Llama 3.3 70B? Open the PC Builder for the 70B tier →

Related models

Qwen2.5 72B72B parameter model — shared focus: chat, coding, reasoning.Ornith 1.5 35B (A3B)36B parameter model — shared focus: coding, agents, chat.Qwen3.6 35B (A3B)36B parameter model — shared focus: chat, reasoning, long-context.Llama 3.1 405B InstructServer-class assistant evaluation and comparisons against smaller Llama variants.

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