Ollama vs llama.cpp vs LM Studio: Same-GPU Compare Dataset

By LK Wood IV · Published 2026-08-20 · Updated 2026-08-26 · ~4 min read · St. Louis County, MO

The short version

This page holds two captures on the same retail RTX 5080. On 2026-08-20 I ran Ollama against bare llama.cpp; on 2026-08-26 I ran Ollama against LM Studio, same GGUF bytes on both sides via hard links. Across both captures the pattern holds: every one of these tools is llama.cpp underneath, decode differences on dense models stay inside 11%, and the settings each app ships with move throughput far more than the app choice does.

Downloads: llm-server-compare-2026-08.csv (Ollama vs llama.cpp) and llm-server-compare-2026-08-26-lmstudio.csv (Ollama vs LM Studio, both conditions). CC BY 4.0, per-run decode arrays included.

Capture 2: Ollama 0.32.15 vs LM Studio 0.4.21 (2026-08-26)

Settings normalized on both sides: context 4096, full GPU offload, temperature 0, seed 42, one serving slot, per-run prompt nonces throughout. Decode figures are medians of five runs; long-prefill figures, measured at ~450 prompt tokens, are medians of three with a warmup discarded. The decision-layer writeup is the LM Studio comparison article.

At stock as-installed settings the dense rows barely move, but two defaults bite: LM Studio’s automatic GPU offload held gpt-oss to 186.3 t/s, 23% below its own tuned figure — and a single-variable re-run (stock config, only the offload slider moved to max) recovered the whole gap at 244.9 t/s. Ollama’s app-level context slider set to 256k (clamped to this model’s 131,072 maximum) dropped Llama 3.2 3B from 351 to 102 t/s with the KV cache spilled to CPU. Both are recorded as their own rows in the CSV and raw JSON. Idle app overhead: the Ollama service tree held 78-132 MB of RAM; LM Studio’s GUI plus server held 709-928 MB across quiet-window observations (one 3.1 GB transitional reading mid-unload excluded, kept in the raw JSON). Ollama’s engine is its vendored llama.cpp at commit 9d77fa172; LM Studio’s is its packaged llama.cpp runtime 2.29.1 (CUDA 12) — and both run the model in a llama-server process.

Ollama alone gained 3-10% decode over its own 08-20 numbers here (319→351 on the 3B; 195.8→213.5 on gpt-oss, defaults condition) across six days and fourteen patch-version increments — with the caveat that the 08-20 capture predates this page’s quiet-window discipline, so part of that spread may be ambient. Either way, single-digit decode gaps between these tools do not survive a version bump.

Capture 1: Ollama 0.32.1 vs llama.cpp b10507 (2026-08-20)

² Prompt-dependent on this MoE: a fixed prompt held 232-242 t/s (median 238.5); varied prompts were bimodal with a ~176 slow path (median 235.3), and the published 222 median includes a cold first run. Ollama’s fork ran a flat 195.7-196.3 under the same nonce scheme. Load times are cold-start to first ready response: llama-server was faster on the 3B, Ollama faster on both larger models.

The desktop baseline was 1,642 MiB of GPU memory before any model loaded (nvidia-smi reports MiB; the CSV column is vram_total_mib_median). These figures run 2-7% above our RTX 5080 throughput dataset for the same models because that capture used 500-1,650-token prompts; both pages state their regime.

Method

I kept one server resident at a time, with a fresh process per model and ollama stop plus process-tree kills between configurations. Every request carried a unique nonce prefix, llama-server additionally ran with prompt caching disabled, and generation was pinned to temperature 0, seed 42, 256 tokens (64 on the long-prefill pass), context 4096, full offload. Rates are each server’s own counters; a wall-clock recomputation agreed within 2% on every run (one cold-start exception at 3.6%, disclosed above), and llama-bench served as a third instrument on the 3B (pp512 19,589 ± 2,071 t/s, tg256 367.1 ± 0.7 t/s, raw engine loop). The capture scripts are available on request via hello@techfuelhq.com.

The limits, named. One GPU, one box, three models per capture, and a short-prompt prefill column retained only to document its own overhead artifact. Token counts for identical text differ slightly between tools because each applies its own chat template handling. The decision-layer writeups are Ollama vs llama.cpp and Ollama vs LM Studio; this page is the citable record for both.