LM Studio vs Ollama on an RTX 5080: The Settings Matter More Than the Wrapper

Adapted from LM Studio vs Ollama on an RTX 5080, published on TechFuelHQ.

If you are choosing between LM Studio and Ollama, choose by workflow, not by benchmark folklore.

I tested both on the same RTX 5080, loading the same GGUF bytes wherever the formats allowed it. Ollama was modestly faster at dense-model decode. LM Studio was faster at prompt prefill. Neither wrapper produced the dramatic gap people often attribute to it.

The largest performance differences came from memory settings.

The short version

Use Ollama when the model is infrastructure:

• A background service
• An API for another application
• A Docker-based stack
• Something that should start with the machine and stay out of the way

Use LM Studio when the model is an interactive appliance:

• A graphical model browser
• A chat interface
• Per-model controls exposed in one place
• An OpenAI-compatible local server with visual configuration

On this test system, the applications were within 14% of one another under matched conditions. A single bad setting produced a 23% loss in LM Studio and a 3.4× slowdown in Ollama.

What I measured

Test system:

• RTX 5080 16 GB
• Windows 11
• NVIDIA driver 610.88
• Ollama 0.32.15
• LM Studio 0.4.21 with llama.cpp CUDA runtime 2.29.1
• Context length 4,096
• Full GPU offload
• Temperature 0
• Seed 42
• One serving slot
• Five fresh-prompt runs per decode cell

For Llama 3.2 3B and Qwen 2.5 14B, both applications loaded identical, hash-verified GGUF files.

Model	Application	Decode	Prompt prefill	GPU memory
Llama 3.2 3B Q4_K_M	Ollama	350.5 tok/s	17,862 tok/s	3,760 MiB
Llama 3.2 3B Q4_K_M	LM Studio	316.0 tok/s	20,337 tok/s	3,808 MiB
Qwen 2.5 14B Q4_K_M	Ollama	102.2 tok/s	5,038 tok/s	10,356 MiB
Qwen 2.5 14B Q4_K_M	LM Studio	97.6 tok/s	5,567 tok/s	10,405 MiB
gpt-oss 20B MXFP4	Ollama	213.7 tok/s	9,187 tok/s	13,570 MiB
gpt-oss 20B MXFP4	LM Studio	242.8 tok/s	9,618 tok/s	12,533 MiB

Ollama decoded the two dense models 5-11% faster. LM Studio prefilled every tested model 5-14% faster.

The gpt-oss row is not an identical-file comparison. Ollama ships its own gptoss conversion; LM Studio used the upstream ggml-org MXFP4 GGUF because its runtime rejected Ollama’s architecture-tagged blob. Engine and conversion differences both contribute to that result.

Both applications are packaging the same underlying server

The process captures showed llama-server.exe serving the model on both sides. Ollama runs its vendored build; LM Studio downloads its own llama.cpp runtime.

That common engine explains why the dense-model results land close together. The wrapper affects defaults, model management, memory behavior, and workflow more than it changes the fundamental inference path.

Versions still matter. Ollama’s Llama 3.2 3B result on this machine moved from roughly 319 tok/s to 351 tok/s across fourteen patch releases and six days. A local-LLM benchmark without application and runtime versions is a snapshot without a timestamp.

LM Studio’s automatic offload cost 23%

LM Studio initially ran gpt-oss 20B at about 186 tok/s with its stock automatic GPU-offload setting.

I first normalized context length, slot count, and offload together, which raised the result to roughly 243 tok/s. To isolate the cause, I repeated the stock configuration and changed only the offload slider.

The result was 244.9 tok/s.

Automatic offload had held part of the model on the CPU even though the card could run the tested configuration fully on the GPU. LM Studio did not surface a warning. Moving one slider recovered the entire gap.

Before comparing applications, confirm that LM Studio has not silently chosen partial CPU offload.

Ollama’s context setting caused a 3.4× slowdown

The same class of failure appeared in Ollama.

At a 4,096-token context, Llama 3.2 3B stayed fully GPU-resident and decoded at about 351 tok/s. This machine still had Ollama’s application-level context slider set to 256k from earlier work. Ollama clamped that request to the model’s 131,072-token maximum.

The resulting footprint grew to about 18 GB and split across system RAM and VRAM. Decode fell to 102 tok/s.

Nothing about the model or application version changed. The oversized KV cache alone turned a 351 tok/s result into a 102 tok/s result.

If Ollama is unexpectedly slow:

1. Check the configured context length.
2. Run ollama ps.
3. Confirm that the model and KV cache are not spilling into system RAM.
4. Retest with the context length the workload actually needs.

VRAM was effectively a tie; system RAM was not

Where both applications loaded the same file, GPU-memory use differed by less than 50 MiB:

• 3,760 vs. 3,808 MiB for Llama 3.2 3B
• 10,356 vs. 10,405 MiB for Qwen 2.5 14B

System RAM showed a larger difference.

With no model loaded, the Ollama service tree used 78-132 MB across the quiet-window observations. LM Studio’s GUI and server used 709-928 MB.

With gpt-oss resident, LM Studio’s process tree reported about 13 GB of system RAM alongside its 12.5 GB VRAM allocation. Ollama’s runner sat around 2.1 GB. Much of LM Studio’s total was memory-mapped file cache and therefore evictable, but it still matters on a machine with limited RAM or other services running.

The model files can be shared

LM Studio imported the Ollama blobs for Llama 3.2 3B and Qwen 2.5 14B directly. Using the hard-link option let both applications name the same physical bytes on disk. Hash checks confirmed that the imported files matched Ollama’s stored blobs.

The exception was gpt-oss 20B. Ollama’s blob declares the gptoss architecture used by its fork, while LM Studio’s upstream llama.cpp runtime rejected it as unknown. That model required the upstream ggml-org GGUF on the LM Studio side.

For most GGUF models, running both applications does not require maintaining two full weight libraries.

Which should you install?

Choose Ollama if you want a lightweight service with simple API and container integration. Its dense-model decode edge was real on this system, and its idle RAM footprint was much smaller.

Choose LM Studio if you want visual model discovery, a chat interface, and granular per-model controls. Its prompt-prefill performance was consistently better here, and the GUI makes configuration easier to inspect, provided you verify the automatic offload decision.

Install both if your work genuinely uses both modes. They coexisted on this card on ports 11434 and 1234, and two resident 3B models used 7,000 MiB of total GPU memory including the desktop baseline.

The actionable result is not that one wrapper is universally faster. It is this:

The application gap was at most 14%. A wrong memory setting was worth up to 240%.

Check GPU offload. Check context length. Record the application and runtime versions. Then benchmark the workflow you actually run.

The complete methodology, raw-result links, RAM observations, model hashes, limitations and the same-GPU comparison dataset are in the full LM Studio vs. Ollama comparison linked at the top.