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Llama 3.1 Community LicenseOpen weights where releasedUpdated August 2026

Llama 3.1 405B Instruct

Large Llama 3.1 instruct model for teams evaluating high-capacity open-weight assistant deployments.

Meta · Llama

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesExact model card

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Server-class assistant evaluation and comparisons against smaller Llama variants.

Who should use it

  • Server-class assistant evaluation and comparisons against smaller Llama variants.
  • Teams with access to hosted inference or server-class deployment paths.

Common workflows

  • Large open-weight assistant evaluation
  • chat workflows
  • large workflows
  • open weights workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Llama 3.1 Community License. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for Llama workflows.
  • Server-class assistant evaluation and comparisons against smaller Llama variants.

Limitations

  • Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
  • Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
  • Context window and limits: 131,072 tokens (RoPE-extended).
  • Verify the exact model card, provider docs, license, and serving support before production use.

Will Llama 3.1 405B Instruct run on your machine?

Llama 3.1 405B Instruct is 405B parameters and needs 246.5 GB of VRAM at Q4_K_M — 245 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_M245 GB246.5 GBgood
Q8_0430 GB431.5 GBhigh

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 GBToo large
Apple Silicon (Unified Memory) 96 GB72 GB of 96 GB96 GBToo large
Apple Silicon (Unified Memory) 128 GB96 GB of 128 GB128 GBToo large
Apple Silicon (Unified Memory) 192 GB144 GB of 192 GB192 GBToo large

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 3.1 405B Instruct against your exact hardwareHow these verdicts are calculated

Need more hardware for Llama 3.1 405B Instruct? Open the PC Builder for the MoE / frontier-style experiment tier →

VRAM fit by quantization level

Enter your GPU VRAM below to see which quantization of Llama 3.1 405B Instruct fits and get the Ollama run command.

Sources to verify

Exact model card

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Llama family hubBest local LLM toolsPrivate local chatbot stackOllama + Open WebUI starter stackModels directory
Hardware~246.1 GB at Q4_K_M (405.9B parameters) — server class, not a single-GPU modelRuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext131,072 tokens (RoPE-extended)Last updated2026
Exact model card →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

What is quantization? GGUF, Q4, Q8 explained →RunPod — managed GPU cloud →Lambda Labs — A100 / H100 on demand →Vast.ai — crowdsourced GPU marketplace →

Related model pages

Llama family hub →Llama 3.1 70B Instruct →Llama 3.1 8B Instruct →Llama 3.2 Vision →Llama 3.3 70B Instruct →Llama 3.3 70B →Llama 3 8B →

Guides, stacks, and comparisons

Best local LLM tools →Private local chatbot stack →Ollama + Open WebUI starter stack →All model comparisons →

Related models

Llama 3.1 70B InstructTeams comparing widely supported Llama-family 70B-class models.Llama 3.1 8B InstructLocal prototypes, small assistants, and lower-resource evaluation.Llama 3.2 VisionVision-language experiments, screenshot reasoning, and multimodal app prototypes.Llama 3.3 70B InstructGeneral assistant workflows, app prototypes, and Llama-family baseline comparisons.Llama 3.3 70B70B open-weight model in the Llama family.Llama 3 8B8B open-weight model in the Llama family.

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