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Models

Llama-3.1-405B

By meta-llama. 405.9 billion parameters, a context window of Unknown tokens and the licence llama3.1.

Facts

Released
2024-07-16 the day the repository was first published on Hugging Face Hugging Face, read
Licence
llama3.1 the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository, behind the maker's access form Hugging Face, read
Parameters
405.9 billion counted from the safetensors weight files Hugging Face, read
Active parameters
Unknown
Knowledge cutoff
Unknown
Context window
Unknown
Longest output
Unknown
Tool calling
Unknown
Structured output
Unknown
Reasoning controls
Unknown
Inputs and outputs
text in, text out Hugging Face, read
Good for
No source names a use.

Prices

No source lists a price for this model. It may only run on your own hardware.

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
IQ1_S79.4 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ1_M87.1 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ2_XXS99.9 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ2_XS111 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ2_S117 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ2_M127 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q2_K_S128 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q2_K139 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ3_XXS145 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ3_XS155 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q3_K_S163 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ3_S163 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ3_M169 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q3_K_M182 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q3_K_L198 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ4_XS202 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
IQ4_NL213 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q4_K_S215 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q4_K_M226 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
F16756 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers

Start it with a local runtime

  • Ollama
    ollama run hf.co/ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S
  • LM Studio
    lms get ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF
lms server start
  • llama.cpp server
    llama-server -hf ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S --jinja
  • vLLM
    vllm serve meta-llama/Llama-3.1-405B
  • SGLang
    sglang serve --model-path meta-llama/Llama-3.1-405B --port 30000

Use it from your harness

Set up for Ollama with the model hf.co/ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S. Each endpoint page has the same setup for its own address.

OpenCode

Put this in opencode.json in your project folder:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Ollama",
      "options": {
        "baseURL": "http://localhost:11434/v1"
      },
      "models": {
        "hf.co/ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S": {
          "name": "Llama-3.1-405B"
        }
      }
    }
  }
}
  • OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.

From OpenCode documentation, read .

Pi

Put this in ~/.pi/agent/models.json:

{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        {
          "id": "hf.co/ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S"
        }
      ]
    }
  }
}
  • The apiKey field can name an environment variable as $NAME.

From Pi documentation, read .

Codex

Put this in a terminal:

codex --oss --local-provider ollama -m hf.co/ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S
  • Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.

From Codex documentation, read .

Claude Code

Put this in ~/.claude/settings.json, or variables in your shell:

export ANTHROPIC_BASE_URL="http://localhost:11434"
export ANTHROPIC_AUTH_TOKEN="ollama"
export ANTHROPIC_MODEL="hf.co/ThomasBaruzier/Meta-Llama-3.1-405B-Instruct-GGUF:Q4_K_S"
claude
  • Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.

From Claude Code documentation, read .

Published results

These are results other people published. Baltor did not run them and does not rank models by them.