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Atlas Inference Engine vs Ollama

Bottom line

Atlas Inference Engine is free, while Ollama has a free plan plus paid upgrades.

Which one should you try?

Atlas Inference Engine

Free

Best for Teams with Nvidia DGX Spark hardware

Visit Atlas Inference Engine See full review

Ollama

Freemium

Best for Developers who want a local model API without managing model files by hand

Visit Ollama See full review

Field notes

All field notes →

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Atlas Inference Engine

Atlas Inference Engine

The vendor page benchmarks Atlas at 3.1x the decode throughput of vLLM on Nvidia DGX Spark hardware — 111 tok/s average versus 37 tok/s on Qwen3.5-35B, with a cold start measured in two minutes instead of ten. That gap exists because Atlas ships no Python, no PyTorch, and no JIT warm-up: every path from HTTP request to kernel dispatch is compiled. The tradeoff is hardware specificity — hand-tuned CUDA kernels target Blackwell SM120/121, so teams not running DGX Spark get none of the headline numbers. The model matrix covers Qwen, Gemma, Nemotron, Mistral, and MiniMax, but every recipe is written for that hardware profile. Teams running other GPU generations are not the audience.

Ollama

Ollama

Ollama downloads open models and runs them locally behind a command line and API, with optional cloud-hosted models. We ran it on a 3-core CPU server and measured startup, memory and speed.

AttributeAtlas Inference EngineOllama
PricingFreeFreemium
Price—$20/mo or $200/yr
Pricing checkedNot checked yetNov 3, 2026
Free trialNot statedNot stated
Open sourceYesYes
Has APIYesYes
Self-hosted optionYesYes
PlatformsLinux (Ubuntu 22.04+) with NVIDIA GPU support (Blackwell GB10 primary, Hopper/Ampere in development)macOS, Windows, Linux, Docker
Released—2023-06
Pros
  • ~2.5 GB container image with no Python or PyTorch dependencies, which means cold starts take two minutes instead of ten — a difference that compounds across every iteration in an agentic development loop.
  • Compiled Rust + CUDA architecture with no GIL or JIT warm-up, so request latency is consistent from the first token rather than degrading during the warm-up window that costs vLLM its first several minutes.
  • Hand-tuned CUDA kernels per model family with NVFP4 and FP8 on Blackwell tensor cores, so quantized inference does not trade throughput for accuracy the way a generic quantization layer would.
  • Multi-Token Prediction speculative decoding built in, so a single DGX Spark node serving a 35B model reaches throughput that would otherwise require additional hardware or a more complex multi-node setup.
  • OpenAI-compatible API endpoint out of the box, so existing tooling — Claude Code, Cline, Open WebUI — connects without a translation layer or custom client code.
  • Pulls and runs a model with one command and serves it over a local API.
  • Quick cold start in our test: about 8 seconds from server start to first answer.
  • OLLAMA_NO_CLOUD=1 turns off its cloud models and web search for local-only use.
  • Generated text faster than LM Studio on the same model and cores, by up to 1.38x with short prompts.
Cons
  • Every published benchmark and kernel optimization targets Nvidia Blackwell SM120/121 on DGX Spark. Teams running Ampere, Ada, or Hopper GPUs get none of the headlined throughput numbers — the architecture constraint is not a tuning issue, it is baked into the kernel design. Those teams are still on vLLM or TensorRT-LLM.
  • The model matrix is a curated, hand-tuned list — Qwen, Gemma, Nemotron, Mistral, MiniMax — not an open registry. A team that needs to serve a fine-tuned model outside that matrix hits a wall immediately and either waits on the Atlas roadmap, opens a Discord request, or returns to vLLM where arbitrary HuggingFace checkpoints load without curation.
  • AGPL-3.0 is the default license. Any team building a closed-source product or operating a SaaS service on top of Atlas is required to obtain a commercial license. Teams that discover this constraint after building on the free version face a licensing conversation before they can ship.
  • Ignores a container's CPU limit: it started 8 threads on 3 cores, and generation was 3.3 to 11.9 times slower until we set num_thread to 3.
  • Caps context at the model's trained length (32,768 tokens for our model) even when you ask for more, so check what you actually get.
  • The largest Docker image of the runtimes we tested: 9.37 GB, against 1.21 GB for llama.cpp.

Made up your mind?

Atlas Inference Engine

Free

Best for Teams with Nvidia DGX Spark hardware

Visit Atlas Inference Engine See full review

Ollama

Freemium

Best for Developers who want a local model API without managing model files by hand

Visit Ollama See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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