How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Llama 3.1 405B on a consumer GPU?

Llama 3.1 405B is a 405B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is Llama 3.1 405B's context window?

Llama 3.1 405B supports a maximum context window of 131,072 tokens. The default context is 4,096 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Llama 3.1 405B?

For Llama 3.1 405B, start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Llama 3.1 405B compare to similar models?

Llama 3.1 405B is a 405.0B Dense model from the Llama family. Browse other Llama models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

LlamaDenseLlama Community License

Llama 3.1 405B

Llama 3.1 405B is a dense transformer language model from the Llama family, containing 405B parameters across 126 layers. It supports up to 131K tokens of context with a hidden dimension of 16384 and 8 KV heads for efficient grouped-query a…

405.0B

Parameters

128K

Max Context

Dense

Architecture

—

Released

Text

Modality

About Llama 3.1 405B

ResearchEnterprise

Technical Specifications

Total Parameters405.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 16,384

Transformer Layers126

Attention Heads128

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	128K ctx
Q4_K_M0.50 B/W ~97% of FP16	209.8Cluster / Multi-GPU	272.3Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	419.2Cluster / Multi-GPU	481.7Cluster / Multi-GPU
F162.00 B/W Reference	837.8Cluster / Multi-GPU	900.4Cluster / Multi-GPU

Params

109.0B

Layers

Context

256K

17.0B active

Find the right GPU for Llama 3.1 405B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

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