Benchmarking Meta Llama 4 Scout on CPU-Only Systems: Performance, Quantization, and Architecture Tuning

Meta’s Llama 4 Scout, released in April 2025, is a 17-billion parameter general-purpose language model that brings powerful reasoning to a broader range of applications—including those running without GPUs.

This blog focuses on benchmarking Llama 4 Scout on CPU-only systems, covering:

1. Tokens per second

2. Latency per token

3. Prompt handling efficiency

4. Quantization techniques

5. Architecture-specific optimization for x86, ARM, and RISC-V (RV64)

6. Converting to GGUF format for efficient deployment

Why Benchmark on CPU?

While most LLMs are deployed on GPUs, CPU-only inference is often necessary for:

• Edge devices

• Cloud VMs with no GPU access

• Open hardware ecosystems (e.g., RISC-V)

• Cost-conscious deployments

That makes Llama 4 Scout a strong candidate, especially with quantized variants.

Key Benchmark Metrics

Tokens/sec	Overall throughput, critical for long completions
Latency/token	Time to generate one token; important for chats
Prompt size sensitivity	How inference speed degrades with longer inputs
Memory usage	RAM footprint determines if the model can run at all

Why Quantization Is Essential

Quantization reduces the memory and compute requirements of large models. Llama 4 Scout quantized to int4 or int8 can run comfortably on CPUs with 8–16 GB of RAM.

Benefit: Impact on Llama 4 Scout

Memory savings: From 34GB → ~5–7GB (int4)

Speedup: Up to 3× faster than float16

Hardware fit: Allows ARM & RV64 CPUs to host inference

Tools like ggml, llama.cpp, and MLC support quantized Llama 4 models, including CPU backends.

Architecture-Specific Performance Considerations

🔹 x86-64 (Intel, AMD)

Vector Support: AVX2 or AVX-512 preferred

Threading: Mature OpenMP and NUMA support

Performance: High; well-optimized in llama models

ARM (Graviton, Apple Silicon, Neoverse)

Vector ISA: NEON (128-bit) on all, SVE/SVE2 on newer chips

Threading: Requires tuning due to core heterogeneity

Quantization: NEON handles int8 and int4 efficiently

Tip: Use taskset and numactl to pin threads for optimal performance.

RISC-V (RV64 with RVV)

Vector ISA: RISC-V Vector Extension (RVV), variable width

Quantization: Essential; float32 models are impractical on RV64 edge devices

Tooling: llama.cpp support is experimental but growing

For RV64, memory layout and cache-friendly quantization are critical due to limited bandwidth.

Sample Inference Results (Hypothetical)

Architecture	Model Variant	Prompt Size	Tokens/sec.	RAM Usage
x86_64	Llama 4 Scout int4	512	11.2	~6.5 GB
ARM Neoverse	Llama 4 Scout int4	512	8.7	~6.5 GB
RISC-V RV64	Llama 4 Scout int4	512	3.2	~6.5 GB

These results assume multi-threaded CPU inference with quantized weights using llama.cpp or similar.

From Raw Model to GGUF: Why and How?

To run Meta Llama 4 Scout efficiently on CPU-only systems, especially with tools like llama.cpp, the model must be in GGUF format.

Why Convert to GGUF?

GGUF (Grokking GGML Unified Format) is a compact, memory-optimized model file format designed for CPU and edge inference using:

llama.cpp

mlc-llm

text-generation-webui

GGUF Advantage : Benefit

Memory Efficient: Packs quantized weights and metadata

Fast Load Times:No need to re-tokenize or parse configs

Metadata Preserved:Tokenizer, vocab, model type included

Simplified Use: Single file usable across many tools

How to Convert Llama 4 Scout to GGUF

1. Download the Raw Model (HF Format)

Get the original model from Hugging Face (e.g., meta-llama/Meta-Llama-4-Scout-17B).

Install transformers and llama-cpp-python tools

pip install transformers huggingface_hub git clone https://github.com/ggerganov/llama.cppcd llama.cppmake

2. Run the GGUF Conversion Script

From the llama.cpp/scripts directory:

python convert.py \ --outfile llama4-scout.gguf \--model meta-llama/Meta-Llama-4-Scout-17B \ --dtype q4_0

3. Load It in Your Inference Tool

Once converted, the .gguf file can be run directly:./main -m llama4-scout.gguf -p "Hello, world"

GGUF + Quantization = CPU Superpowers

Converting to GGUF enables you to quantize during the conversion:

q4_0, q4_K, q5_1, and q8_0 supported

You reduce size dramatically—from ~34GB → ~5–7GB for q4

It ensures compatibility with CPU SIMD instructions like AVX, SVE, or RVV

On RISC-V or ARM boards with limited memory, GGUF + int4 is often the only way to get Llama 4 Scout running at all.

Pro Tip: GGUF Conversion Options

You can fine-tune conversion settings:

--vocab-type to customize tokenizer structure

--trust-remote-code if the Hugging Face repo uses custom loading

--quantize q4_K for better int4 accuracy

Final Thoughts

Meta's Llama 4 Scout is one of the most practical open-source LLMs for CPU inference in 2025. With quantization and SIMD-aware deployment, it can serve:

Edge applications (IoT, phones)

Sovereign compute platforms (RISC-V)

Cloud-native environments without GPUs

If you’re interested in pushing the limits of open LLMs on CPU architectures, Llama 4 Scout is one of the best starting points.