vLLM vs llama.cpp Use Cases | Generated by AI
Question: Why do people use vLLM? Compare vLLM to llama.cpp.
Answer:
People use vLLM mainly for high-throughput serving, while llama.cpp is used for local lightweight inference. They target different workloads.
Core Difference (one sentence)
- vLLM β server for many concurrent users, high throughput π
- llama.cpp β run locally on small hardware, minimal setup π§
Architecture Difference
vLLM
- GPU-first inference engine
- Uses PagedAttention (KV cache paging)
- Batching across users
- Optimized for API serving
- Needs large GPU VRAM
llama.cpp
- CPU-first (GPU optional)
- No dynamic batching
- Single/few users
- GGUF quantization
- Runs on laptop / small GPU
Performance Comparison
| Feature | vLLM | llama.cpp |
|---|---|---|
| Multi-user | β Excellent | β Poor |
| Throughput | β Very high | β Low |
| Single-user latency | β οΈ medium | β very good |
| CPU support | β No | β Excellent |
| GGUF support | β No | β Native |
| Memory efficiency | β οΈ medium | β best |
| Large model serving | β best | β οΈ limited |
| RTX 4070 friendly | β οΈ limited | β best |
| Production API | β ideal | β οΈ basic |
| Tool calling | β good | β good |
Why companies use vLLM
Because vLLM can:
1. Serve many users at once
Example:
- 50 users chatting simultaneously
- vLLM batches them
- GPU utilization ~90%
- 10x throughput
llama.cpp:
- processes sequentially
- GPU idle often
- much slower overall
2. OpenAI-compatible API server
vLLM provides:
vllm serve model
Then:
POST /v1/chat/completions
Used by:
- OpenWebUI
- OpenClaw
- LangChain
- AutoGen
- agents
3. Much faster batching
Example:
Single request:
- llama.cpp faster
10 requests:
- vLLM much faster
100 requests:
- vLLM orders of magnitude faster
Why individuals use llama.cpp
Because it:
1. Runs on small GPUs
You can run:
- 26B IQ3
- 34B IQ2
- even 70B CPU
On RTX 4070:
- llama.cpp β yes
- vLLM β impossible
2. Supports GGUF
All these work:
Q4_K_M
IQ3_S
IQ2_XS
vLLM cannot load them.
3. Much lower VRAM
Example 26B:
| Engine | VRAM |
|---|---|
| vLLM | ~30GB |
| llama.cpp IQ3 | ~13GB |
Huge difference.
When to use vLLM
Use vLLM if:
- building API server
- multi-user chat
- agent swarm
- high throughput
- GPU β₯ 24GB
- datacenter environment
When to use llama.cpp
Use llama.cpp if:
- local development
- RTX 4070 / 4060
- GGUF models
- single user
- offline usage
- memory limited
Your case (RTX 4070)
You should prefer:
π llama.cpp Not vLLM
Because:
- 12GB VRAM
- want 26B models
- want GGUF
- single user
vLLM gives no advantage for you.
Simple Analogy
vLLM = restaurant kitchen π½οΈ
- cooks many orders together
- efficient at scale
llama.cpp = home cooking π³
- perfect for one person
- simple, flexible
Final Recommendation
For your hardware:
RTX 4070 + local models β llama.cpp wins
Use vLLM only if you upgrade to:
- RTX 4090
- A100
- 3090
- multi GPU server