Llama 4 Maverick 17B 128E Instruct (Q5_K_S) — 49.3 GBon NVIDIA RTX 3090

Q5_K_S NVIDIA RTX 3090

Overview

Llama 4 Maverick 17B 128E Instruct is a 396.58B parameter moe language model by Meta, with code, multilingual, tool-calls, vision capabilities. It supports a context window of up to 1,048,576 tokens.

Llama 4 Maverick 17B 128E Instruct is a large-scale Mixture-of-Experts model from Meta with 17 billion parameters per expert and 128 experts, activating one expert per token for a total of approximately 400 billion parameters. It delivers frontier-class performance on vision, code generation, and multilingual tasks across 12 languages. Maverick represents the high-capacity tier of the Llama 4 family, trading higher memory requirements for stronger benchmark results. With a 1M token context window, it requires multi-GPU setups but quantizes down to Q2 levels.

At Q5_K_S quantization (medium quality tier), the model weighs 256.77 GB. This exceeds the 24 GB of VRAM on NVIDIA RTX 3090. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.

The NVIDIA GeForce RTX 3090 is a consumer GPU with 24 GB of GDDR6X VRAM and 936 GB/s memory bandwidth. It provides 35.6 FP16 TFLOPS, offering solid performance for local LLM inference at a lower cost than newer cards. It runs quantized models up to 20B parameters well. A practical choice for budget-minded developers and home lab enthusiasts.

Hardware Requirements

Model size	256.77 GB
VRAM available	24 GB
VRAM used	49.3 GB
Min RAM required	256.8 GB
GPU layers	0 / 48
Context size	262,144
Backend	cuda13
Flash attention	No

Performance Notes

Mixture-of-Experts

This is a Mixture-of-Experts model with 128 total experts, of which 1 are active per token. Only the active experts need fast GPU access — inactive expert weights can reside in system RAM with minimal impact on generation speed.

Read the MoE deployment guide →

Reduced performance

48 of 48 layers run on CPU. Your system needs at least 256.8 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-4-maverick-17b-128e-instruct/q5_k_s/nvidia-rtx3090.yaml) apply

Generated values.yaml

/values/llama-4-maverick-17b-128e-instruct/q5_k_s/nvidia-rtx3090.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-4-maverick-17b-128e-instruct.gguf "https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/resolve/main/Q5_K_S/Llama-4-Maverick-17B-128E-Instruct-Q5_K_S-00001-of-00006.gguf"

Start Server

llama-server \
  -m llama-4-maverick-17b-128e-instruct.gguf \
  --n-gpu-layers 0 \
  --ctx-size 262144

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Llama 4 Maverick 17B 128E Instruct (Q8_0)
• Llama 4 Maverick 17B 128E Instruct (Q5_K_M)
• Llama 4 Maverick 17B 128E Instruct (Q4_K_M)

Other hardware

• Llama 4 Maverick 17B 128E Instruct on CPU Only
• Llama 4 Maverick 17B 128E Instruct on NVIDIA A100 80GB
• Llama 4 Maverick 17B 128E Instruct on NVIDIA H100

Frequently Asked Questions

How much VRAM does Llama 4 Maverick 17B 128E Instruct (Q5_K_S) need?

The Q5_K_S quantization of Llama 4 Maverick 17B 128E Instruct requires 256.77 GB. The 24 GB of VRAM on NVIDIA RTX 3090 is insufficient for GPU layers, so inference runs on CPU.

Can I run Llama 4 Maverick 17B 128E Instruct on NVIDIA RTX 3090?

It is possible but not recommended. NVIDIA RTX 3090 does not have enough VRAM to accelerate Llama 4 Maverick 17B 128E Instruct (Q5_K_S), so inference will rely on CPU and system RAM.

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. Q5_K_S compresses Llama 4 Maverick 17B 128E Instruct from its original size down to 256.77 GB.

What quantization should I choose for Llama 4 Maverick 17B 128E Instruct?

Q5_K_S is a medium-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.

Why are some layers offloaded to CPU?

NVIDIA RTX 3090 has 24 GB of VRAM, but Llama 4 Maverick 17B 128E Instruct (Q5_K_S) requires approximately 256.77 GB. Only 0 of 48 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

What is MoE and how does it affect deployment?

Llama 4 Maverick 17B 128E Instruct uses a Mixture-of-Experts (MoE) architecture with 128 experts, of which 1 are active per token. This means only a fraction of the model weights are used for each inference step, allowing MoE models to be larger in total parameter count while remaining efficient at inference time.

How do I run Llama 4 Maverick 17B 128E Instruct (Q5_K_S) with Ollama?

Run ollama run llama4:17b-maverick-128e-instruct-q5_k_s to start Llama 4 Maverick 17B 128E Instruct (Q5_K_S). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026