Skip to content
Locai is now Aventail™Learn about the rebrand
Aventail™

Aventail™ Link vs Ollama

Model
Gemma 4 E2B-it
Quantisation
Q4_K_M
Load
Single stream
Measured

Both runtimes served the same Gemma 4 E2B-it Q4_K_M model, one request at a time, with identical sampling. These results were measured and originally published in June 2026 under the product’s former name, Locai Link. They have not been rerun on current releases and describe only the machines and settings recorded here, not performance in general.

Hardware
Workload

Showing Linux conversation results.

Linux · NVIDIA RTX 4070Conversation

Prompt
~30 tokens
Reply
~300 tokens
Runs
20 (+3 warmup)
Errors
0

Short conversational prompt (~30 tokens), mid-length reply (~300 tokens).

Aventail™ Link measured better
time to first token, median; time to first token, p95; prefill throughput; decode throughput, median; decode throughput, p95; end-to-end latency, median
Ollama measured better
peak VRAM; peak host RSS
Selected results, Linux conversation. Bars start at zero; exact values shown.

Time to first token, median (ms) lower is better

Link
31 ms
Ollama
326 ms

Decode throughput, median (tok/s) higher is better

Link
196 tok/s
Ollama
166 tok/s

End-to-end latency, median (s) lower is better

Link
1.6 s
Ollama
2.1 s

Peak host RSS (MB) lower is better

Link
5,799 MB
Ollama
3,022 MB
All metrics, Linux NVIDIA RTX 4070, conversation
MetricAventail™ LinkOllamaLink − Ollama
Time to first token, median (ms)lower is better31326−295 ms (Link better)
Time to first token, p95 (ms)lower is better132338−206 ms (Link better)
Prefill throughput (tok/s)higher is better93692+844 tok/s (Link better)
Decode throughput, median (tok/s)higher is better196166+30 tok/s (Link better)
Decode throughput, p95 (tok/s)higher is better198169+29 tok/s (Link better)
End-to-end latency, median (s)lower is better1.62.1−0.5 s (Link better)
Peak VRAM (MiB)lower is better4,2441,990+2,254 MiB (Ollama better)
Peak host RSS (MB)lower is better5,7993,022+2,777 MB (Ollama better)

Linux · NVIDIA RTX 4070Document analysis

Prompt
~9,500 tokens
Reply
≤500 tokens
Runs
10 (+3 warmup)
Errors
0

Long-context document prompt (~9,500 tokens) from 'From the Earth to the Moon', mid-length reply (≤500 tokens).

Aventail™ Link measured better
time to first token, median; prefill throughput; decode throughput, median; decode throughput, p95; end-to-end latency, median
Ollama measured better
peak VRAM; peak host RSS
Selected results, Linux document analysis. Bars start at zero; exact values shown.

Time to first token, median (ms) lower is better

Link
56 ms
Ollama
385 ms

Decode throughput, median (tok/s) higher is better

Link
174 tok/s
Ollama
158 tok/s

End-to-end latency, median (s) lower is better

Link
1.2 s
Ollama
1.6 s

Peak host RSS (MB) lower is better

Link
6,003 MB
Ollama
3,091 MB
All metrics, Linux NVIDIA RTX 4070, document analysis
MetricAventail™ LinkOllamaLink − Ollama
Time to first token, median (ms)lower is better56385−329 ms (Link better)
Time to first token, p95 (ms)lower is betterNot reportedNot reportedNot reported
Prefill throughput (tok/s)higher is better169,46624,728+144,738 tok/s (Link better)
Decode throughput, median (tok/s)higher is better174158+16 tok/s (Link better)
Decode throughput, p95 (tok/s)higher is better178160+18 tok/s (Link better)
End-to-end latency, median (s)lower is better1.21.6−0.4 s (Link better)
Peak VRAM (MiB)lower is better4,2662,006+2,260 MiB (Ollama better)
Peak host RSS (MB)lower is better6,0033,091+2,912 MB (Ollama better)

Memory trade-off: Ollama used less peak VRAM and host memory than Link in both scenarios.

Both workloads on Linux compared
  • Time to first token, median, Link vs Ollama: 31 vs 326 ms (conversation); 56 vs 385 ms (document analysis). Link better in both scenarios; lower is better.
  • Decode throughput, median, Link vs Ollama: 196 vs 166 tok/s (conversation); 174 vs 158 tok/s (document analysis). Link better in both scenarios; higher is better.
  • End-to-end latency, median, Link vs Ollama: 1.6 vs 2.1 s (conversation); 1.2 vs 1.6 s (document analysis). Link better in both scenarios; lower is better.
  • Peak VRAM, Link vs Ollama: 4,244 vs 1,990 MiB (conversation); 4,266 vs 2,006 MiB (document analysis). Ollama better in both scenarios; lower is better.
  • Peak host RSS, Link vs Ollama: 5,799 vs 3,022 MB (conversation); 6,003 vs 3,091 MB (document analysis). Ollama better in both scenarios; lower is better.

The original report attributes the narrower decode gap to both backends sharing the same underlying CUDA kernels for the generation loop.

On memory, the original web page says Ollama defers KV-cache and weight allocations more aggressively while Locai Link pre-allocated them; the PDF report describes the gap as different KV-cache strategies, with Link allocating a larger fixed cache.

Linux environment and source notes (includes a build discrepancy)

Source discrepancy: llama.cpp build: the original web page data records b9789, while the original raw JSON and the PDF report both record b9222. We have not chosen between them, so the exact Link build used for these results cannot be confirmed.

Machine

Operating system
Arch Linux
GPU
NVIDIA RTX 4070, 12,288 MiB VRAM
GPU driver
610.43.02
CUDA
13.3
Compiler
nvcc / g++-15
Reasoning
Off (LLAMA_ARG_REASONING=off / think: false)

Aventail™ Link (then Locai Link)

Backend
llama.cpp, built from source (build number conflicts between sources: b9789 or b9222)
Compute
CUDA
API
/v1/chat/completions, OpenAI-compatible SSE

Ollama

Package
ollama-cuda (Arch package)
Settings
OLLAMA_LLM_LIBRARY=cuda_v13, num_gpu=999 (request GPU offload)
Compute
CUDA
API
/api/chat, NDJSON

Not specified in the source: Ollama release version.

Original note: Single-stream measurements only. One request in flight at a time per backend. Concurrent load was not measured. Results are specific to Arch Linux and the ollama-cuda Arch package; build steps and library paths differ on Ubuntu and RHEL.

macOS · Apple M2, 16 GBConversation

Prompt
~30 tokens
Reply
~300 tokens
Runs
20 (+3 warmup)
Errors
0

Short conversational prompt (~30 tokens), mid-length reply (~300 tokens).

Aventail™ Link measured better
time to first token, median; time to first token, p95; prefill throughput
Ollama measured better
decode throughput, median; decode throughput, p95; end-to-end latency, median; peak host RSS
Selected results, macOS conversation. Bars start at zero; exact values shown.

Time to first token, median (ms) lower is better

Link
205 ms
Ollama
478 ms

Decode throughput, median (tok/s) higher is better

Link
39 tok/s
Ollama
41 tok/s

End-to-end latency, median (s) lower is better

Link
7.9 s
Ollama
7.8 s

Peak host RSS (MB) lower is better

Link
6,493 MB
Ollama
3,332 MB
All metrics, macOS Apple M2, 16 GB, conversation
MetricAventail™ LinkOllamaLink − Ollama
Time to first token, median (ms)lower is better205478−273 ms (Link better)
Time to first token, p95 (ms)lower is better215490−275 ms (Link better)
Prefill throughput (tok/s)higher is better14160+81 tok/s (Link better)
Decode throughput, median (tok/s)higher is better3941−2 tok/s (Ollama better)
Decode throughput, p95 (tok/s)higher is better4041−1 tok/s (Ollama better)
End-to-end latency, median (s)lower is better7.97.8+0.1 s (Ollama better)
Peak VRAM (MiB)lower is betterNot applicable — unified memoryNot applicable — unified memoryNot applicable
Peak host RSS (MB)lower is better6,4933,332+3,161 MB (Ollama better)

macOS · Apple M2, 16 GBDocument analysis

Prompt
~9,500 tokens
Reply
≤500 tokens
Runs
10 (+3 warmup)
Errors
0

Long-context document prompt (~9,500 tokens) from 'From the Earth to the Moon', mid-length reply (≤500 tokens).

Aventail™ Link measured better
time to first token, median; prefill throughput; decode throughput, p95
Ollama measured better
end-to-end latency, median; peak host RSS
Equal
decode throughput, median
Selected results, macOS document analysis. Bars start at zero; exact values shown.

Time to first token, median (ms) lower is better

Link
332 ms
Ollama
589 ms

Decode throughput, median (tok/s) higher is better

Link
30.4 tok/s
Ollama
30.4 tok/s

End-to-end latency, median (s) lower is better

Link
5.5 s
Ollama
5.3 s

Peak host RSS (MB) lower is better

Link
6,759 MB
Ollama
3,429 MB
All metrics, macOS Apple M2, 16 GB, document analysis
MetricAventail™ LinkOllamaLink − Ollama
Time to first token, median (ms)lower is better332589−257 ms (Link better)
Time to first token, p95 (ms)lower is betterNot reportedNot reportedNot reported
Prefill throughput (tok/s)higher is better28,69316,178+12,515 tok/s (Link better)
Decode throughput, median (tok/s)higher is better30.430.4No difference
Decode throughput, p95 (tok/s)higher is better32.331.9+0.4 tok/s (Link better)
End-to-end latency, median (s)lower is better5.55.3+0.2 s (Ollama better)
Peak VRAM (MiB)lower is betterNot applicable — unified memoryNot applicable — unified memoryNot applicable
Peak host RSS (MB)lower is better6,7593,429+3,330 MB (Ollama better)

Memory trade-off: Ollama’s peak memory was roughly half of Link’s in both scenarios. On Apple Silicon the GPU shares system memory, so there is no separate VRAM figure; host RSS captures the full footprint.

Both workloads on macOS compared
  • Time to first token, median, Link vs Ollama: 205 vs 478 ms (conversation); 332 vs 589 ms (document analysis). Link better in both scenarios; lower is better.
  • Decode throughput, median, Link vs Ollama: 39 vs 41 tok/s (conversation); 30.4 vs 30.4 tok/s (document analysis). conversation: Ollama better; document analysis: equal; higher is better.
  • End-to-end latency, median, Link vs Ollama: 7.9 vs 7.8 s (conversation); 5.5 vs 5.3 s (document analysis). Ollama better in both scenarios; lower is better.
  • Peak host RSS, Link vs Ollama: 6,493 vs 3,332 MB (conversation); 6,759 vs 3,429 MB (document analysis). Ollama better in both scenarios; lower is better.
macOS environment and source notes

Machine

Operating system
macOS
Hardware
Apple M2 SoC · 16 GB unified memory
Compute
Metal (Apple Silicon)
Reasoning
Off (LLAMA_ARG_REASONING=off / think:false)

Aventail™ Link (then Locai Link)

Backend
llama.cpp (Metal)
Compute
Metal
API
/v1/chat/completions, OpenAI-compatible SSE

Ollama

Package
ollama (native macOS build)
Compute
Metal
API
/api/chat, NDJSON

Not specified in the source: Ollama release version, macOS version, Locai Link / llama.cpp build (the report says only that Locai Link’s installer downloaded a Metal-accelerated llama.cpp prebuilt).

Original note: Single-stream measurements on Apple Silicon (M2, 16 GB unified memory). Concurrent load not measured. On Apple Silicon the GPU shares the system memory pool — there is no separate VRAM, so RSS captures the full memory footprint.

Windows

Windows: no published results in this dataset. The Linux results should not be read as a prediction of Windows performance.

Method and downloads

Original files

Unmodified copies of the files published with the June 2026 results. The macOS results were published as a report only; no macOS raw run data is available.

Shared settings and limits
Model
Gemma 4 E2B-it, 4.6B parameters, Q4_K_M
File
GGUF, 2.9 GB
Architecture
Sliding-window attention, shared KV layers
Sampling
temperature = 0, top_p = 1, seed = 42, streaming = true
Load
Single stream: one request in flight at a time per backend. Concurrent load was not measured.

Output equivalence

The original report records: “Token-identical replies at temperature=0 confirmed for all prompts across both backends.” This applies to the tested prompts only and is not a general guarantee of output quality.

Source checks and discrepancies

Every value on this page matches the original web page data. The Linux raw JSON and the Linux and macOS PDF reports carry the same values. The reports’ ratios are derived from these values, and their distribution and per-run charts are images without published figures, so neither adds new measurements here. No benchmarks were rerun for this page.

  • Linux: llama.cpp build: the original web page data records b9789, while the original raw JSON and the PDF report both record b9222. We have not chosen between them, so the exact Link build used for these results cannot be confirmed.
  • The original web page omits the reasoning setting for Linux; the raw JSON and report record it as off on both backends.
  • The measurement date is June 2026, as recorded in the sources. The upload date of the macOS report file is not used as a measurement date.

We use optional cookies and tracking to identify visiting companies, load our contact form, measure Google Ads download clicks and understand use of our private AI page with PostHog. Cookie policy