Ollama vs vLLM: which LLM runtime for production?

Choosing between Ollama and vLLM means balancing deployment simplicity against raw throughput under concurrent load. This debate now shapes a large share of self-hosted infrastructure decisions around open-weights LLMs, whether you're serving an internal agent or exposing a multi-tenant API. Here, we'll compare the two runtimes in terms of architecture, measurable throughput on NVIDIA GPUs, memory management (KV cache, PagedAttention), supported licenses and formats, operating costs, and recommendations by model size, followed by a technical FAQ. Our goal: enable a platform engineer to make a decision in under fifteen minutes of reading.

Architecture and philosophy: two runtimes, two worlds

Ollama is a wrapper around llama.cpp, written in Go, which exposes a simple HTTP API and handles downloading, caching, and dynamic loading of GGUF models. Its historical target: developer machines (M-series Macs, desktop RTX PCs) and lightweight single-tenant deployments. The underlying runtime performs transparent CPU offloading, allowing you to run a Qwen 2.5 72B Instruct (Q4 VRAM ~42 GB) on a RTX 4090 24 GB with system RAM swap, at the cost of throughput.

vLLM, published by UC Berkeley, is a Python server optimized for high-concurrency serving. Its key contribution is PagedAttention, which manages the KV cache like an OS manages virtual memory: fixed-size blocks, paging, and sharing between requests. In practice, where a naive runtime wastes 60–80% of the KV cache through fragmentation, vLLM falls below 4%. The result is 2x to 24x higher aggregate throughput depending on the workload, but with an entry cost: no native GGUF quantization, a strict CUDA dependency, and the entire model in VRAM.

To explore the difference in approach between llama.cpp and vLLM for serving, see our complete llama.cpp vs vLLM guide.

Throughput and latency: what the numbers say

Public benchmarks converge on one point: under concurrent load, vLLM wins by a wide margin. On a Llama 3.3 70B Instruct (70B, 128,000 context) served in FP8 on 4× A100 80 GB, typically we observe (to be confirmed depending on batch size):

The gap widens further with MoE models such as Mixtral 8x22B Instruct (141B, Apache 2.0, 64K ctx), where vLLM leverages multi-GPU tensor parallelism with an efficiency that llama.cpp struggles to match. For very large MoEs such as DeepSeek V3 671B or Qwen 3 235B-A22B, vLLM remains the only realistic choice for multi-user production, notably thanks to native support for expert parallelism.

However, for a single-stream workload with aggressive quantization (Q4_K_M, Q5_K_M), Ollama holds its own on time to first token (TTFT), and is sometimes faster on 7B–13B models thanks to the lightweight runtime. For a detailed benchmark on consumer GPUs, see our RTX 4090 vs. RTX 5090 comparison for LLMs.

Quantization, formats, and actual VRAM

This is where the two runtimes diverge radically. Ollama uses exclusively GGUF (Q2_K to Q8_0, plus FP16). This flexibility makes it possible to fit a Llama 3.1 70B (~40 GB in Q4) on a single 48 GB RTX A6000. vLLM accepts native HuggingFace weights (FP16, BF16) and now supports AWQ, GPTQ, and FP8, but not GGUF for stable production use.

Some Q4 VRAM guidelines from the BestLLMfor catalog:

For modest deployments, gpt-oss 120B and Mistral Small 4 offer a sensible compromise: manageable size, permissive license, support for both runtimes. See our guide Mistral Small 4 for detailed benchmarks.

Licensing and compliance

The runtime choice has no licensing impact — it is the model which sets the terms. A few typical cases in European production:

For a complete overview, see our open-source LLM licensing guide and the community tracking at HuggingFace Models.

Use cases: which one should you choose for what?

Choose Ollama if: - You’re deploying an internal assistant on a developer workstation or a single GPU workstation - You want to iterate quickly across multiple models (hot-swapping via API) - Your workload has < 5 concurrent users - You’re targeting a RTX 3090/4090/5090 or an M3 Ultra Mac Studio - You want to test dots.llm1 Instruct (142B, MIT, Rednote) or Hunyuan-A13B Instruct without configuring a cluster

Choose vLLM if: - You serve an API behind a load balancer with > 20 requests/sec - You run tensor parallelism on 2, 4, or 8 GPUs - You want to use PagedAttention, speculative decoding, and continuous batching - You target massive MoE models: DeepSeek V3.2 (685B), Mistral Large 3 675B, Llama 3.1 405B Instruct, Ring-1T (1000B) - You need Llama 4 Scout 109B with its 10M-token context in multi-tenant serving

A third option is worth mentioning: Text Generation Inference from HuggingFace, midway between the two, natively supported for Mistral Medium 3.5 128B and the family Llama. To orchestrate a vLLM cluster, Ray Serve remains the reference.

For GPU-budget recommendations, see our configurator guide and the page best LLM for a GPU server.

Operating Costs and Observability

Ollama offers greater operational simplicity: a single binary, minimal telemetry, and one-command model updates. The hidden cost is the lack of fine-grained metrics (no native Prometheus support in recent stable versions, to be confirmed). vLLM natively exposes a Prometheus endpoint with time-to-first-token, throughput per request, and KV-cache utilization, and integrates directly with Grafana.

In terms of GPU cost, a well-tuned vLLM cluster reduces the cost per million tokens by a factor of 3 to 8 compared with an equivalent Ollama multi-instance deployment—the difference comes from continuous batching and the near absence of KV fragmentation. On Qwen3-Coder-Next 80B-A3B (MoE, Apache 2.0), our readers report (to be confirmed) ~14,000 aggregated tokens/sec on 2× H100 in vLLM, versus ~80 tokens/sec single-stream on Ollama with an RTX 6000 Ada.

To go further with tuning, see the Official vLLM blog and our vLLM vs. TGI comparison.

FAQ

Q: Can Ollama serve multiple simultaneous users in production?

Technically yes, but with limitations. Ollama handles multiple requests through an internal queue, without efficient continuous batching. Beyond 3–5 concurrent users on a 70B model such as Llama 3.3 70B Instruct, p95 latency degrades significantly. For serious multi-tenant production, vLLM or TGI remain the technically justified choices.

Q: Does vLLM support aggressive quantizations such as Q4 GGUF?

Not in native GGUF. vLLM favors FP8, AWQ, and GPTQ, which offer different quality/VRAM trade-offs. On DeepSeek R1 671B, an official 4-bit AWQ version is available on HuggingFace and runs in vLLM. For strict GGUF Q4_K_M, stick with Ollama or llama.cpp directly — that is their domain.

Q: Which runtime should I use for an MoE like Qwen 3 235B-A22B?

vLLM, without hesitation. MoE models benefit massively from the expert parallelism and continuous batching that vLLM implements natively. Qwen 3 235B-A22B (Apache 2.0, 131K context) achieves an aggregate throughput that Ollama cannot approach, even on an 8× H100 cluster. See our profile Qwen 3 235B-A22B.

Q: Can Ollama be used on a Kubernetes cluster?

Yes, community Helm charts exist, but Ollama was not designed for stateless horizontal scaling. Each pod reloads its models, and the GGUF cache is not shared. For native Kubernetes deployments, vLLM integrates better via KServe and its dedicated operator.

Q: Which runtime should you choose for Llama 4 Scout 109B and its 10M context?

vLLM with sparse attention enabled. The 10M context of Llama 4 Scout 109B requires KV-cache management that only PagedAttention makes economically viable. Ollama can technically load the model, but the KV cache at 10M tokens makes VRAM usage explode without pagination. Reserve this for vLLM or TGI.

Q: Is there an alternative to the two for Apple Silicon Macs?

Yes: MLX Apple is optimized for Metal and outperforms Ollama on the M3 Ultra for models such as DeepSeek R1 Distill Llama 70B. But multi-user Mac serving remains niche. See our LLM guide for Mac M3 Ultra.

Conclusion

The Ollama vs. vLLM debate comes down to the target workload: Ollama for prototyping, single-user workloads, and Macs; vLLM for multi-tenant APIs, massive MoE models, or tensor parallelism. To identify the model/runtime pair suited to your VRAM and target license, run our configurator or browse the 249 models indexed on the BestLLMfor catalog.

Article published on by Mohamed Meguedmi · Data source: /api/models.json · Content license: CC BY 4.0.

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