Running frontier open-weight models locally has transitioned from a hobbyist curiosity into a mission-critical infrastructure tier for software teams, enterprise labs, and privacy-focused engineers. While single-user desktop runtimes like Ollama (powered by llama.cpp) popularized effortless model execution on personal PCs, multi-agent frameworks, coding co-pilots, and local API servers demand concurrent throughput that standard single-sequence engines cannot sustain. Modern high-throughput inference runtimes—primarily UC Berkeley's vLLM and LMSYS's SGLang—introduce continuous batching, non-contiguous KV-cache memory management (PagedAttention), and hierarchical prefix caching (RadixAttention). In this exhaustive technical hardware benchmark, we measure Ollama, vLLM, and SGLang across single and dual RTX 4090/5080 workstations to evaluate tokens-per-second concurrency, Time-To-First-Token (TTFT) latency, and VRAM memory efficiency.
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1. Architectural Foundations: llama.cpp vs PagedAttention vs RadixTree
To understand why these engines exhibit drastically different scaling curves under concurrency, we must examine their underlying memory allocation designs:
| Architectural Metric | Ollama (llama.cpp) | vLLM (v0.6+ PagedAttention) | SGLang (RadixAttention) |
|---|---|---|---|
| Primary Serving Goal | Single-user low latency, Apple Silicon & CPU fallback | High-throughput concurrent server API serving | Complex multi-turn agent workflows & structured JSON |
| Memory Allocation | Static contiguous memory arena for sequence context | PagedAttention (Virtual memory blocks, zero fragmentation) | RadixTree LRU KV Cache with PagedAttention backend |
| Multi-GPU Scaling | Layer-offload splitting (Pipeline Parallelism only) | Native Tensor Parallelism (TP) via NCCL | Native Tensor Parallelism (TP) via NCCL |
| Prefix Caching Re-use | Limited sequential prompt cache dumping | Automatic Prefix Caching (Hash-based block matching) | RadixAttention (Tree structure, instant multi-turn re-use) |
| Quantization Formats | GGUF (Q4_K_M, Q8_0, IQ3, etc.) | AWQ, GPTQ, BitsAndBytes, FP8, FP16 | AWQ, GPTQ, BitsAndBytes, FP8, FP16 |
Ollama's underlying llama.cpp runtime relies on contiguous physical memory allocations for each conversation context. When processing concurrent requests, memory fragmentation wastes up to 40% of available GPU buffer space. In contrast, vLLM treats KV cache identically to virtual memory operating systems: splitting token keys and values into 16-token memory blocks mapped non-contiguously. SGLang advances this paradigm further with RadixAttention, maintaining an exact LRU Radix Tree of previously computed prompt prefixes across multiple conversational turns, eliminating redundant prefill compute.
2. Single-GPU vs Multi-GPU Tensor Parallelism Benchmarks
We configured our test bench with dual NVIDIA GeForce RTX 4090 GPUs (24GB GDDR6X each, 48GB combined buffer) connected via PCIe 4.0 x8 lanes on an AMD TRX50 workstation motherboard. We benchmarked Llama 3.3 70B Instruct (AWQ 4-bit) and Qwen 2.5 Coder 32B Instruct across 1, 4, 16, and 32 concurrent requests with an input prompt of 2,048 tokens and an output generation of 512 tokens:
# Throughput Benchmark: Qwen 2.5 Coder 32B (Dual RTX 4090, 48GB VRAM)
# Metric: Total Generated Tokens Per Second (Aggregate Throughput)
Concurrency Level: 1 Client (Single Stream Latency)
- Ollama (Q4_K_M GGUF): 44.2 tokens/sec (TTFT: 142 ms)
- vLLM (AWQ 4-bit, TP=2): 58.7 tokens/sec (TTFT: 98 ms)
- SGLang (AWQ 4-bit, TP=2): **63.4 tokens/sec** (TTFT: 82 ms)
Concurrency Level: 8 Parallel Clients (Multi-Agent Swarm Load)
- Ollama (Q4_K_M GGUF): 52.1 tokens/sec (Queued, high contention)
- vLLM (AWQ 4-bit, TP=2): 284.6 tokens/sec (Continuous batching)
- SGLang (AWQ 4-bit, TP=2): **318.2 tokens/sec** (Continuous batching + Radix Cache)
Concurrency Level: 32 Parallel Clients (Enterprise API Stress Test)
- Ollama (Q4_K_M GGUF): CRASH / OOM (Failed contiguous allocation)
- vLLM (AWQ 4-bit, TP=2): 412.8 tokens/sec (Zero OOM, stable PagedAttention)
- SGLang (AWQ 4-bit, TP=2): **461.5 tokens/sec** (Zero OOM, highest saturation)
The benchmark reveals a dramatic operational divergence: While Ollama is exceptional for a single developer typing into a chat terminal (delivering 44 tokens/sec immediately), its throughput hits an architectural ceiling under parallel load because llama.cpp queues requests sequentially or incurs massive memory overhead. vLLM and SGLang achieve nearly **8x higher aggregate throughput** under 32 concurrent clients by processing token generations in continuous, dynamic micro-batches.
3. RadixAttention & Multi-Turn Agent Speedups
In coding assistants and agentic loops (such as Claude Code, AutoGen, or CrewAI), the system prompt, repository tree, and conversational history remain identical across consecutive turns, while only a small user instruction changes. We measured the prefill latency (Time to First Token) on an 8,000-token system context:
| Inference Engine | Turn 1 (Cold Prefill TTFT) | Turn 2 (Warm Cached TTFT) | Prefill Compute Speedup |
|---|---|---|---|
| Ollama (llama.cpp) | 620 ms | 410 ms | 1.51x faster |
| vLLM v0.6 (Prefix Caching) | 480 ms | 92 ms | 5.21x faster |
| SGLang (RadixAttention) | 440 ms | 38 ms | 11.57x faster |
SGLang's RadixAttention reduces Time-to-First-Token on warm requests from 440 milliseconds down to 38 milliseconds. Because the Radix Tree retains the token nodes in GPU memory across independent API requests without serializing to disk, agentic loops respond almost instantaneously.
4. Step-by-Step Production Deployment Commands
Here are the exact command lines to launch each runtime on Linux/WSL2 with optimal GPU parameters:
# 1. Launch SGLang with Tensor Parallelism on 2x GPUs (Best for Agents & Concurrency):
python3 -m sglang.launch_server \
--model-path Qwen/Qwen2.5-Coder-32B-Instruct-AWQ \
--tp 2 \
--port 30000 \
--mem-fraction-static 0.88 \
--context-length 32768 \
--enable-prefix-caching
# 2. Launch vLLM with PagedAttention and OpenAI-compatible API:
vllm serve Qwen/Qwen2.5-Coder-32B-Instruct-AWQ \
--tensor-parallel-size 2 \
--gpu-memory-utilization 0.90 \
--max-model-len 32768 \
--enable-prefix-caching \
--port 8000
# 3. Launch Ollama (Best for quick single-user desktop testing):
OLLAMA_NUM_PARALLEL=4 ollama run qwen2.5-coder:32b
5. Technical Verdict: Which Runtime Fits Your Architecture?
**Stick with Ollama if:** You run a single laptop or workstation (especially Apple Silicon M-series Macs), prioritize zero-configuration terminal interaction, and only execute single-user conversational prompts.
**Deploy vLLM if:** You are serving high-concurrency production web applications, require rock-solid Docker container orchestration, or need broad compatibility with enterprise cloud platforms.
**Deploy SGLang if:** You operate autonomous multi-agent systems, complex multi-turn coding pipelines, or structured JSON schema workflows where RadixAttention's sub-50ms prefix caching unlocks maximal GPU efficiency.
Originally published on NextByte Tech — Modern computing, hardware optimization & AI workflows.
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