Skip to content
llm-speed

Flint-40B-A2.5B-UD-ctx8k on RTX 4090 (24GB)

MeasuredOn 2026-10-22, Flint-40B-A2.5B-UD-ctx8k Q3_K_XL ran at 168.0 tok/s (chat-short) on an RTX 4090 (24GB) with llama.cpp. Run r_r7gzl4lmmdc.

RTX 4090 (24GB)RTX 4090 (24GB)
suite suite-v1
cli 0.0.7
signedyomriqNF38…
Embed badge@llmspeedsubmitted Oct 22, 2026

Workload results

WorkloadBackendModeldecode tok/sprefill tok/sTTFTp50p95
chat-shortllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-ctx8kQ3_K_XL168.0tok/s488.4tok/s201ms5.8ms6.2ms
chat-longllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-ctx8kQ3_K_XL157.6tok/s5,789.2tok/s521ms6.3ms6.4ms
concurrent-decodellama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-ctx8kQ3_K_XL166.5tok/s——5.9ms6.0ms
agent-tracellama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-ctx8kQ3_K_XL160.3tok/s10,408.2tok/s184ms6.1ms6.3ms

Reproduce on your machine

Start with this model and workload selection. This command does not capture the original artifact, backend version or server settings:

Match quantization, context capacity, caching and runtime settings for a useful comparison. Duration varies by workload and hardware; the published client may differ from this run's version. How it's measured.

Share this run

Copy the short link, a Reddit-ready summary, or a README badge. Every number is the measured result on this page, and each badge links back to it.

llm-speed: 168.0 tok/s on RTX 4090 (24GB) (Flint-40B-A2.5B-UD-ctx8k)
https://llm-speed.com/r/r_r7gzl4lmmdc

Related benchmarks

Provenance

Run ID
r_r7gzl4lmmdc
Fingerprint hash
Public key
yomriqNF38I9OAc6Aj1hZBvog2KHoeXVhn5NPJ5WohI=
Received
2026-10-22 01:27:09