How much VRAM does Llama 3.1 70B need?

Llama 3.1 70B 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 70B on a consumer GPU?

Llama 3.1 70B is a 70.6B 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 70B's context window?

Llama 3.1 70B supports a maximum context window of 131,072 tokens. The default context is 8,192 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 70B?

For Llama 3.1 70B, 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 70B compare to similar models?

Llama 3.1 70B is a 70.6B 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 3.1 Community License

Llama 3.1 70B

70.6B

Parameters

128K

Max Context

Dense

Architecture

Jul 23, 2024

Released

Text

Modality

About Llama 3.1 70B

Llama 3.1 70B was the open-weight model to beat through late 2024 and most of 2025. At 70.6B parameters it delivers GPT-4-class performance on reasoning, coding, and creative tasks. At Q4_K_M it requires ~38 GB VRAM — fitting on a single RTX 5090 (32 GB) only with partial offloading. Most users run it at Q3_K_M (~30 GB) on 24 GB GPUs with ~6 GB offloaded to system RAM. The quality jump from 8B to 70B is substantial for complex reasoning, multi-step instruction following, and long-form generation. Still a strong choice where ecosystem maturity matters.

ReasoningCodeCreative WritingRAGAgentic

Technical Specifications

Total Parameters70.6B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 8,192

Transformer Layers80

Attention Heads64

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE (theta=500,000)

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	36.80Datacenter GPU	76.49Datacenter GPU
Q8_01.00 B/W ~100% of FP16	73.30Datacenter GPU	113.0Cluster / Multi-GPU
F162.00 B/W Reference	146.3Cluster / Multi-GPU	186.0Cluster / Multi-GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other Llama Models

View All

Llama 3.2 1B

Params

1.2B

Layers

Context

128K

Llama 3.2 3B

Params

3.2B

Layers

Context

128K

Llama 3.1 8B

Params

8.0B

Layers

Context

128K

Llama 3.1 405B

Params

405.0B

Layers

126

Context

128K

Llama 3.3 70B

Params

70.6B

Layers

Context

128K

Llama 4 Scout (MoE)

Params

109.0B

Layers

Context

256K

17.0B active

Find the right GPU for Llama 3.1 70B

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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