LM Studio vs Ollama on an RTX 5080: Measured (2026)

Depending on which search result you trust, LM Studio is 46% faster than Ollama, 10-20% slower, or exactly the same speed. The Reddit thread that ranks first for this query carries “3-4x faster” claims for Ollama, while a Stack Exchange question a few results down asks why Ollama’s models run faster inside LM Studio. Those claims cannot all be true on the same computer, so I put both apps on mine: one RTX 5080, the same GGUF files loaded by both. Hard-linked, sha256-verified, the same bytes on disk. Five runs per cell, at stock settings and again with settings matched.

The apps are within 14% of each other everywhere. One wrong setting is worth 240%.

The verdict, by use case

Neither app deserves a speed-based religion. Here is what one controlled rig actually measures.

What people claim vs what I measured

The top of this search results page disagrees with itself. The r/LocalLLaMA thread that ranks first carries claims of Ollama being “3-4x” faster; a Medium post and Codiste both say Ollama wins by 10-20%; atomic.chat says LM Studio generates 46% faster; tech-insider.org titles its piece “Zero Performance Difference [Tested]”, then prints Qwen 2.5 14B Q4_K_M at 25-30 tokens/sec on an RTX 4090, attributed to community benchmarks. Every one of those pages is linked in the Sources block. My RTX 5080 measures that exact model and quant at 97-102 t/s in both apps. Three to four times their 4090 figure. A different machine and unknown settings on their side, granted, and that is precisely the problem — this argument runs on numbers nobody measured under stated conditions.

With settings matched (context 4096, full GPU offload, temperature 0, seed 42, one serving slot, medians of five fresh-prompt runs):

² Different conversions of the same model, because they have to be: Ollama ships its own gpt-oss blob and LM Studio runs the ggml-org upstream MXFP4 file. More on that below.

Ollama decodes faster on dense models, by 10.9% on the 3B and 4.7% on the 14B. LM Studio prefills faster on everything I tested and wins the MoE outright. Each side wins one event, which is the same shape my Ollama vs llama.cpp bench found six days earlier, and none of it justifies “3-4x” folklore. The per-run decode arrays, VRAM, RAM, LM Studio TTFT medians, and load times are in the same-GPU compare dataset, CC BY 4.0.

Same engine underneath, same process name

The process tables captured with every run show the same thing on both sides: the model being served by a llama-server.exe process — Ollama’s from its install directory, LM Studio’s from its backend extension directory. Both apps are packaging around llama.cpp’s server, which is why the dense-model numbers land within 11% of each other and why “which app is faster” is mostly a question about versions and defaults.

The versions matter more than the wrapper. Ollama 0.32.15 runs its vendored llama.cpp at commit 9d77fa172; LM Studio 0.4.21 downloaded its llama.cpp runtime 2.29.1 on first launch. The installer shipped 2.28.2 engines, still on disk next to the newer one. The app updated itself before I ran anything. Worth knowing if you pin versions for reproducibility. Six days and fourteen patch-version increments separate my two captures of Ollama on this rig, and its 3B decode moved from 319 t/s (0.32.1, August 20) to 351 (0.32.15, August 26). Some of that is engine updates. Some may be capture-window conditions, since the earlier run predates the quiet-window discipline described in the method. By the time I finished writing, Ollama had already promoted 0.33.0. Any LM-Studio-vs-Ollama benchmark without version numbers is a screenshot of a moving target.

The defaults are the real story

The plan for this piece treated “defaults as installed” and “settings normalized” as separate test conditions on the expectation that packaging differences would show up in the numbers, and the dense models shrugged at every bit of it: Qwen 2.5 14B ran 98.2 t/s at LM Studio’s stock settings and 97.6 normalized while the 3B moved about 1%. When the model fits comfortably, stock settings are fine. Stop tuning.

Two defaults are not fine, and both fail silently.

LM Studio’s automatic GPU offload quietly benched gpt-oss at 186 t/s — 23% below the 243 the same app hits with settings tuned. Stock, LM Studio loads models at 8,192 context across 4 parallel slots with GPU offload on “auto”, and my first normalized pass changed all three at once. So I re-ran the stock configuration changing only the offload slider, nothing else. Result: 244.9 t/s, the entire gap, from one setting. Context length and slot count contributed nothing measurable. On a 16 GB card, auto had chosen to hold part of the MoE’s weights on the CPU, and no warning appears anywhere. Setting GPU offload to max was worth 31% on this card.

Ollama’s context slider is a 3.4x foot-gun. Ollama’s documented default for a 16 GB card is a 4,096-token context, and at that default the 3B decodes at 351 t/s, 100% GPU-resident. This machine’s Ollama had its app-settings context slider at 256k from earlier use, a value Ollama clamps to this model’s 131,072-token maximum. Still far past what fits. With nothing else changed, the same 2 GB model loaded an 18 GB footprint, split 22%/78% across CPU and GPU, and decoded at 102 t/s. A 3.4x slowdown from one setting, and ollama ps is the only place that shows you why. I would bet money this explains a large share of the “Ollama was crawling for me” posts in that Reddit thread; it is also presumably invisible to anyone benchmarking only fresh installs.

If one number from this page survives in your memory, make it this pair. The gap between these two apps is at most 14%. The gap between a good and a bad memory setting is 240%.

Memory: VRAM is a tie, RAM is not

Wherever both apps loaded the identical file, GPU memory matched within 50 MiB — 3,760 vs 3,808 on the 3B, 10,356 vs 10,405 on the 14B. Same bytes, same engine, same memory. The gpt-oss row differs by 1,037 MiB because the conversions differ; Ollama’s fork has allocated about a gigabyte more than upstream in every capture I have run, on either engine.

System RAM is where the apps genuinely part ways. Idle with no model, the Ollama service tree held 78-132 MB across my checks; LM Studio’s GUI and server held 709-928 MB across the quiet-window observations (one transitional reading hit 3.1 GB mid-unload and is excluded as a non-idle state; it is in the raw JSON). Loaded, LM Studio’s runtime keeps the memory-mapped model file hot in its working set. With gpt-oss resident, its process tree reported about 13 GB of RAM alongside the 12.5 GB of VRAM, where Ollama’s runner released its mapping and sat near 2.1 GB. Mapped file pages are evictable cache, so this is not a hard requirement, but on a machine with 16-32 GB of RAM running other work, Ollama is the lighter tenant: five to six times lighter loaded, five to twelve times lighter idle. tech-insider.org’s “5x Memory Gap” headline has the right direction and, at idle, roughly the right magnitude; the spread depends mostly on what the Electron GUI is doing.

Load times were closer than I expected: 2.3-2.8s for the 3B on both apps, 5.5s vs 7.7s on the 14B, 8.1s vs 8.4s on gpt-oss, warm caches throughout.

The portability catch travels

My llama.cpp piece found that Ollama’s gpt-oss:20b blob declares architecture gptoss, which upstream llama.cpp rejects. LM Studio reproduces the refusal verbatim: importing the blob works, the file indexes, and loading it dies with unknown model architecture: 'gptoss' from its 2.29.1 runtime. So the one model Ollama co-launched still cannot leave Ollama, now confirmed against two external consumers of the file.

Everything else travels beautifully. lms import took Ollama’s Llama 3.2 and Qwen 2.5 blobs by path, and with the hard-link option both apps share one physical copy of the weights. I verified it: each blob shows a link count of three (Ollama’s store, my staging copy, LM Studio’s models directory all naming the same bytes), and the files hash identically to Ollama’s own blob digests. The listings ship with the raw evidence. In the other direction, Ollama imports any GGUF with a one-line Modelfile (FROM /path/to/file.gguf and ollama create), though it copies rather than links. Running both apps against one model library costs you nothing but the gpt-oss exception.

Which one belongs on your machine

Run Ollama when the model is infrastructure: a service that starts at boot, feeds Docker containers, answers on 11434 from whatever stack you build on it, and stays out of RAM when idle. The local-LLM tutorials on this site ride on it for exactly that reason, and its dense-decode edge is real if modest.

Run LM Studio when the model is an appliance: you want to browse Hugging Face quants visually, flip a sampling parameter without editing a Modelfile, watch token streams in a chat pane, and still get an OpenAI-compatible server on 1234 when an app needs one. Its per-model load settings are the granular control Ollama hides behind environment variables, the same control that makes the auto-offload default worth checking before you benchmark anything.

Run both when the fits above split your week. They coexist on one card, share model files one direction cleanly, and the second app costs you a few hundred megabytes of idle RAM. Measured with both 3Bs resident: 7,000 MiB of total GPU memory including the desktop’s ~1.1 GB baseline, and sequential requests answered at 298 and 230 t/s. That is lower than the solo medians, though those coexistence runs capped generation at 64 tokens, so they are not directly comparable to the 256-token table above. My VRAM calculator and speed calculator both apply unchanged to either app, because underneath they are the same engine. That is the finding of this whole exercise. Pick by workflow. Fix your settings. The by-GPU model guide covers what fits in your VRAM either way.

Method and limits

Both apps measured 2026-08-26 on the same box, an RTX 5080 16 GB on driver 610.88 under Windows 11. Ollama 0.32.15, upgraded from 0.32.1 the same morning — the newest release at bench time, with 0.33.0 promoted while this was being written and not tested here. LM Studio 0.4.21, installed fresh that morning, runtime self-updated to llama.cpp-cuda12 2.29.1. Models: Llama 3.2 3B Q4_K_M (blob dde5aa3fc5ff…), Qwen 2.5 14B Q4_K_M (blob 2049f5674b1e…), gpt-oss 20B MXFP4 — Ollama’s blob e7b273f96360… on its side, ggml-org’s upstream file on LM Studio’s side, whose sha256 27cd6c432c76… matches the hash Hugging Face publishes. Rates come from each app’s own counters (Ollama’s eval fields; LM Studio’s /api/v0 stats), cross-checked by wall-clock recomputation. Decode figures are medians of five runs, long-prefill figures medians of three (a fourth warmup run is discarded), every run carrying a unique prompt nonce to defeat prompt caches. The gpt-oss offload finding was re-verified with a single-variable run — stock configuration, only the offload slider moved — recorded with the rest of the raw JSON. LM Studio’s prefill rate is derived from time-to-first-token minus one decoded token, because its API does not split prompt evaluation out the way Ollama’s does; the long-prefill TTFTs live in the raw JSON captures if you want to re-derive. One app under test at a time, the other fully killed, except in the coexistence check.

What I did not test: Mac and MLX (where the loudest “LM Studio is faster” claims come from, and where nothing on this page applies), AMD cards, multi-GPU, LM Studio’s chat UI as opposed to its server, concurrent multi-request serving, and NVFP4 quantization — no NVFP4 conversion of this model set existed to test. Mid-capture I caught a background indexing job on my own machine depressing 3B decode by 15%; it was suspended and every published number re-measured on a quiet system, which is one more reason to distrust any benchmark that does not say what else the box was doing. All raw JSON captures live in the repo, and the dataset page is the citable record. These numbers carry version stamps because they moved 3-10% in two weeks on one of the apps alone; date everything, including mine.