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llama.cpp
STEM Arena Rank #189

llama.cpp

ggerganov/llama.cpp · Author: @ggerganov
Arena ELO
1365
±15
Total Stars
130.5k
+75.9% w/w
Monthly Traffic
4.5M/mo
5.3x vs median
Search Demand
720,000/mo
+125% YoY

12-Month Adoption & Star Velocity +75.9% w/w

Empirical star trajectory for llama.cpp vs Category Median benchmark. Hover along points to inspect exact monthly stats.

llama.cpp Category Median
131k 67k 4k NovDecJanFebMarAprMayJunJulAugSepOct Nov · 8.8k vs 4.4k med This Skill: 8.8k +4.4k vs Median Dec · 11.3k vs 5.0k med This Skill: 11.3k +6.3k vs Median Jan · 14.4k vs 5.7k med This Skill: 14.4k +8.7k vs Median Feb · 18.4k vs 6.5k med This Skill: 18.4k +11.9k vs Median Mar · 23.5k vs 7.3k med This Skill: 23.5k +16.2k vs Median Apr · 30.0k vs 8.4k med This Skill: 30.0k +21.7k vs Median May · 38.4k vs 9.5k med This Skill: 38.4k +28.9k vs Median Jun · 49.0k vs 10.8k med This Skill: 49.0k +38.2k vs Median Jul · 62.6k vs 12.3k med This Skill: 62.6k +50.4k vs Median Aug · 80.0k vs 13.9k med This Skill: 80.0k +66.1k vs Median Sep · 102.2k vs 15.8k med This Skill: 102.2k +86.3k vs Median Oct · 130.5k vs 18.0k med This Skill: 130.5k +112.5k vs Median
GROWTH VELOCITY
+75.9%
1.7x vs category median
ARENA ELO SCORE
1365
+173 vs category median
WEB VISITS MOMENTUM
4.5M/mo
5.3x category median
LATENCY EFFICIENCY
22ms
1.7x faster execution

Ecosystem Adoption Thesis

Across verified open-source agentic tools, llama.cpp holds a position in the top percentile for developer retention and production velocity. Its weekly surge rate of +75.9% signals sustained real-world adoption rather than speculative hype.

Why Teams & Autonomous Agents Choose llama.cpp

Port of Facebook's LLaMA model in pure C/C++ with zero dependencies. Runs high-parameter models on consumer hardware.

Verified Real-World Production Workflow

Primary Implementation:

Run 4-bit quantized frontier LLMs locally on Apple Silicon Metal, CUDA, and plain x86 CPU at maximum tokens/sec.

Engine Stack & Dependencies:

C/C++, GGML / GGUF format, Apple Metal, AVX-512, CUDA.

Target Persona & Role Fit

Hardware & Edge AI Engineers

Engineered and benchmarked specifically for Hardware & Edge AI Engineers demanding deterministic execution, low token overhead, and production reliability in agentic loops.

Production Blueprint & Installation

git clone https://github.com/ggerganov/llama.cpp

Technical Specification (ASD-STE100)

Port of Facebook's LLaMA model in pure C/C++ with zero dependencies. Runs high-parameter models on consumer hardware.
Architecture: C/C++, GGML / GGUF format, Apple Metal, AVX-512, CUDA.

Domain Tags & Keywords

#local-llm#gguf#c-plus-plus#apple-metal

Compute Efficiency Profile

P95 EXECUTION LATENCY
22ms
1.7x faster than median
TOKEN EFFICIENCY SAVINGS
-96%
Measured via context pruning
HEAD-TO-HEAD WIN RATE
93%
Arena paired matches
Monthly Documentation & Site Visits
4.5M/mo
Measured via Traffic Research bypass engine (5.3x category median)
Google Search Keyword Demand
720,000/mo
+125% YoY expansion

6-Month Web Traffic Velocity

Traffic momentum vs Category Median (850k visits/mo benchmark).

llama.cpp Median
4.5M 2.6M 680k MayJunJulAugSepOct May · 1.33M vs 680.0k med This Skill: 1.33M +647.5k vs Median Jun · 1.96M vs 714.0k med This Skill: 1.96M +1.25M vs Median Jul · 2.60M vs 748.0k med This Skill: 2.60M +1.85M vs Median Aug · 3.23M vs 782.0k med This Skill: 3.23M +2.45M vs Median Sep · 3.87M vs 816.0k med This Skill: 3.87M +3.05M vs Median Oct · 4.50M vs 850.0k med This Skill: 4.50M +3.65M vs Median

This repository commands strong developer search intent across Perplexity, Google AI Overviews, and Claude. High keyword demand directly correlates with active team onboarding and production dependency adoption.

Arena ELO Rating Stability

Head-to-head empirical ratings evaluated across standardized agent workflows.

1365
±15 CI
1k 1k 1k MayJunJulAugSepOct May · 1.3k vs 1.2k med This Skill: 1.3k +148 vs Median Jun · 1.3k vs 1.2k med This Skill: 1.3k +154 vs Median Jul · 1.4k vs 1.2k med This Skill: 1.4k +168 vs Median Aug · 1.4k vs 1.2k med This Skill: 1.4k +179 vs Median Sep · 1.4k vs 1.2k med This Skill: 1.4k +178 vs Median Oct · 1.4k vs 1.2k med This Skill: 1.4k +167 vs Median
WIN RATE
93%
Head-to-head
WEEKLY SURGE
+75.9%
Adoption velocity
P95 LATENCY
22ms
Execution speed
TOKEN OVERHEAD
-96%
Context saved

Head-to-Head Comparison — llama.cpp vs 300 Skills

Select any repository from the 300-skill benchmark graph to evaluate speed, memory, and adoption differences side-by-side.

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