Ollama vs. vLLM auf DGX Spark für die Softwareentwicklung

Getting to run large language models like Ollama and vLLM locally on powerful developer workstation hardware like the NVIDIA DGX Spark is one of the most exciting hardware developments I’ve seen in quite a while.

The DGX Spark is a very cool piece of hardware with 128gb of unified memory. Its not the fastest possible chipset for the price in terms of bandwidth (a Mac Studio is 3X faster in theory) but it has unequalled depth of memory and theoretically a 70 billion parameter model quantised to 4-bit fits on to it.

However it does beg a question for the wannabe “local only” software dev. What model works best when you are just getting your feet wet with local LLMs for coding tasks?

The two popular choices that dominate local LLM inference today are Ollama and vLLM. While both excel at running open-weight models locally, my experience is that they serve fundamentally different workflows and performance goals.

In this article I want to share my experiences running both inference servers on the Spark in August 2026 and what daily driver model I chose for coding.

A Quick Note: Why NVIDIA NIM Isn’t in This Comparison

Before diving in, you might wonder why NVIDIA NIM (Inference Microservices) isn’t on this list. While NIM is an impressive enterprise solution built for production clusters and cloud deployments, it comes with enterprise licensing considerations, NGC registry overhead, and strict containerized workflows. As a local developer building and testing on dedicated hardware, I don’t currently need cloud-scale orchestration to get work done. Ollama and vLLM give me full, unconstrained control over my workflow without external dependencies.

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1. Ollama Is Very Easy To Set Up and Run

Ollama has become the default entry point for local LLM execution, and for good reason. Under the hood, Ollama leverages llama.cpp to provide a clean, developer-focused wrapper that abstracts away low-level C++ bindings, quantization configs, and memory management.

Key Strengths

• Zero-Friction CLI & API: Getting a model running is as simple as ollama run llama3. It automatically pulls weights, handles quantizations (GGUF), and exposes an OpenAI-compatible HTTP API on localhost:11434.
• Low Idle Footprint: Ollama unloads models from memory when idle (configurable via timeout), freeing up GPU VRAM for other local tasks.
• Model Management: Modelfiles allow you to customize system prompts, temperature defaults, and context lengths cleanly in a single configuration file.

Limitations

• Lower Concurrency Throughput: Because it relies on llama.cpp‘s architecture, Ollama handles single-user interactive prompts brilliantly, but degrades when bombarded with high concurrent requests.
• Limited Advanced KV-Cache Control: You have less direct control over custom PagedAttention, KV-cache quantization, or tensor parallelism parameters.

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2. vLLM Is Much Faster For Multi User Situations

vLLM was created by UC Berkeley researchers specifically to solve memory bottlenecks in high-concurrency LLM serving. Powered by PagedAttention, vLLM manages Attention Key-Value (KV) memory much like an operating system manages virtual memory with paging.

Key Strengths

• Unmatched Concurrency & Throughput: Through PagedAttention and continuous batching, vLLM achieves 2x to 4x higher throughput under multi-user or parallel request loads compared to traditional serving engines.
• Native Hugging Face Integration: You can point vLLM directly to standard Hugging Face repository IDs or local unquantized/quantized safetensors directories without needing to convert weights to GGUF first.
• Granular Architectural Control: Allows precise tuning of tensor parallelism (--tensor-parallel-size), max model context lengths, KV-cache memory usage percentage (--gpu-memory-utilization), and speculative decoding.

Limitations

• Higher Setup & Memory Overhead: vLLM pre-allocates GPU memory for its KV cache on launch, taking up significant VRAM even when idle.
• Developer Ergonomics: Setting up vLLM requires a Python environment, CUDA dependencies, or Docker container management rather than a simple single-binary installer.

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Comparison Matrix of Ollama Vs vLLM for Coding

Merkmal	Ollama	vLLM
Primary Core Engine	llama.cpp	PyTorch / PagedAttention
Model Weight Formats	GGUF (Quantized)	Safetensors, AWQ, GPTQ, FP8, Unquantized
Primary Use Case	Local scripting, single-user dev, rapid prototyping	Multi-client local API, batch processing, production endpoints
Concurrent Request Batching	Basic (sequential / queued)	Continuous Batching + PagedAttention
Setup Complexity	One-line installer / CLI	Python package (pip install vllm) or Docker
OpenAI API Compatibility	Yes (/v1/chat/completions)	Yes (/v1/chat/completions)
VRAM Management	Dynamic loading/unloading on demand	Pre-allocated KV-cache pool

Which One Should You Choose For Local Coding Tasks?

Choose Ollama if:

• You want an immediate, friction-free setup to test prompts, write local scripts, or build agentic loops on your machine.
• You are running single-user workflows where time-to-first-token (TTFT) for interactive chat matters most.
• You prefer lightweight GGUF quantized models to minimize memory footprint and keep VRAM free when idle.

Choose vLLM if:

• You are turning your DGX Spark into a local network API server for a team or multiple internal services.
• You are executing large batch evaluation jobs, benchmark suites, or synthetic data generation scripts that send dozens of parallel requests.
• You work directly with Hugging Face format weights, custom fine-tunes, or advanced FP8/AWQ quantization schemas without converting to GGUF.

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The model I actually use everyday is……Qwen 3.8 27B

When I first got the DGX Spark, I was really excited to evaluate several different larger models and get first-hand experience on the nuances of each, what each offers, and how different quantizations affect functionality.

In practice today, my daily driver is Qwen 3.8 27B paired with a DeepSeek coding harness. It defaults to overthinking, which makes token generation a bit slower, but it is exceptionally capable when strict instruction following is important. Intuitively, it feels like a bit of a downgrade from 3.6 35B when it comes to nuance or broad real-world knowledge, but its precision for coding tasks makes it my go-to.

This real-world workflow ultimately shaped how I use both inference engines:

• Ollama for Exploration: Any time I want to test a new model, experiment with a candidate architecture, or compare quantization levels, I spin it up in Ollama. The one-command workflow lets me evaluate without touching system configs.
• vLLM for Daily Driving: Once I settled on Qwen 3.8 27B as my primary model, I set it up to be persistently served via vLLM. This keeps my core coding API fast, stable, and ready for my editor harness at all times.

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So long as innovation keeps happening so rapidly and I have the need to keep evaluating new models, I’ll keep Ollama around. The All-in-One (AIO) Open WebUI container couldn’t be easier to get up and running fast. See below for my Docker configs:

services:
  vllm-server:
    image: vllm/vllm-openai:latest
    container_name: vllm-server
    ipc: host
    ulimits:
      memlock: -1
      stack: 67108864
    ports:
      - "8000:8000"
    environment:
      # Enter your actual token directly inside the quotes below
      - HF_TOKEN="your token here"
    volumes:
      - /root/.cache/huggingface/hub:/root/.cache/huggingface/hub
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    command:
      - "--model"
      - "Inferact/Qwen3.8-27B-NVFP4"
      - "--gpu-memory-utilization"
      - "0.5"
      - "--tensor-parallel-size"
      - "1"
      # - "--max-model-len"
      # - "32768"
      - "--enable-chunked-prefill"
      # - "--max-num-batched-tokens"
      # - "8192"
      - "--kv-cache-dtype"
      - "fp8"
      - "--reasoning-parser"
      - "qwen3"
      - "--enable-auto-tool-choice"
      - "--tool-call-parser"
      - "qwen3_coder"
    restart: unless-stopped
    
    
  open-webui:
    image: ghcr.io/open-webui/open-webui:ollama
    container_name: open-webui
    restart: always
    environment:
      - OLLAMA_HOST=0.0.0.0
      - OLLAMA_ORIGINS=*
      - WEBUI_COOKIE_SECURE=False
    ports:
      - "8080:8080"
      - 11434:11434
    volumes:
      - open-webui:/app/backend/data
      - open-webui-ollama:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

Summary: Start with Ollama for daily local data-gathering, prototyping, and exploration. Switch to vLLM the moment you need to serve multiple clients simultaneously or max out batch inference performance.