Llama 3.3 70B Instruct (Q6_K_L) — 23.4 GBon NVIDIA RTX 4090

Q6_K_L NVIDIA RTX 4090

Overview

Llama 3.3 70B Instruct is a 70B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.

Llama 3.3 70B Instruct is a 70-billion-parameter dense transformer model from Meta, optimized for instruction following, code generation, and multilingual conversation. It delivers performance competitive with larger models in the Llama family while remaining practical for single-node GPU deployments. The model supports tool calling and eight languages including English, French, Spanish, and German. With a 128K context window and grouped-query attention, it quantizes efficiently down to Q4 levels for self-hosted inference on consumer hardware.

At Q6_K_L quantization (low quality tier), the model weighs 54.39 GB. This exceeds the 24 GB of VRAM on NVIDIA RTX 4090. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.

The NVIDIA GeForce RTX 4090 is a consumer GPU with 24 GB of GDDR6X VRAM and 1008 GB/s memory bandwidth. It delivers 82.6 FP16 TFLOPS, making it one of the fastest consumer cards for local LLM inference. It handles quantized models up to 20B parameters comfortably. Ideal for home lab builders and developers who want high-throughput inference without datacenter hardware.

Hardware Requirements

Model size	54.39 GB
VRAM available	24 GB
VRAM used	23.4 GB
Min RAM required	32.6 GB
GPU layers	32 / 80
Context size	1,308
Backend	cuda13
Flash attention	Yes

Performance Notes

Reduced performance

48 of 80 layers run on CPU. Your system needs at least 32.6 GB RAM. If system RAM is insufficient, data will be read from disk on demand, with significant performance impact.

Shorter conversations

Context size has been reduced due to memory constraints. This may affect the model's ability to handle long conversations.

Deploy

Server Local

Prerequisites

Ensure your GPU nodes are prepared with the NVIDIA container toolkit:

ansible-playbook prositronic.infra.nvidia_container_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/llama-3-3-70b-instruct/q6_k_l/nvidia-rtx4090.yaml) apply

Generated values.yaml

/values/llama-3-3-70b-instruct/q6_k_l/nvidia-rtx4090.yaml

Loading values…

Install llama.cpp

Install llama.cpp from the official build instructions for your platform and backend.

Download Model

curl -L -o llama-3-3-70b-instruct.gguf "https://huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF/resolve/main/Llama-3.3-70B-Instruct-Q6_K_L/Llama-3.3-70B-Instruct-Q6_K_L-00001-of-00002.gguf"

Start Server

llama-server \
  -m llama-3-3-70b-instruct.gguf \
  --n-gpu-layers 32 \
  --ctx-size 1308 \
  --flash-attn

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Llama 3.3 70B Instruct (FP16)
• Llama 3.3 70B Instruct (Q8_0)
• Llama 3.3 70B Instruct (Q5_K_M)

Other hardware

• Llama 3.3 70B Instruct on CPU Only
• Llama 3.3 70B Instruct on NVIDIA A100 80GB
• Llama 3.3 70B Instruct on NVIDIA H100

Frequently Asked Questions

How much VRAM does Llama 3.3 70B Instruct (Q6_K_L) need?

The Q6_K_L quantization of Llama 3.3 70B Instruct requires 54.39 GB. 32 of 80 layers fit in the 24 GB of VRAM on NVIDIA RTX 4090; remaining layers are offloaded to CPU.

Can I run Llama 3.3 70B Instruct on NVIDIA RTX 4090?

Yes, with reduced performance. NVIDIA RTX 4090 can run Llama 3.3 70B Instruct (Q6_K_L), but only 32 of 80 layers fit in VRAM. The rest are offloaded to CPU.

What is quantization?

Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q6_K_L compresses Llama 3.3 70B Instruct from its original size down to 54.39 GB.

What quantization should I choose for Llama 3.3 70B Instruct?

Q6_K_L is a low-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.

What is flash attention and why is it enabled?

Flash attention is a memory-efficient algorithm that speeds up the attention mechanism in transformer models. It reduces VRAM usage during inference by avoiding the materialisation of the full attention matrix. For Llama 3.3 70B Instruct on NVIDIA RTX 4090, flash attention is enabled to maximise context length and throughput within the available 24 GB of VRAM.

Why are some layers offloaded to CPU?

NVIDIA RTX 4090 has 24 GB of VRAM, but Llama 3.3 70B Instruct (Q6_K_L) requires approximately 54.39 GB. Only 32 of 80 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

How do I run Llama 3.3 70B Instruct (Q6_K_L) with Ollama?

Run ollama run llama3.3:70b-instruct-q6_k_l to start Llama 3.3 70B Instruct (Q6_K_L). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026