Skip to content
llm-speed

Flint-40B-A2.5B-UD-cpumoe6-ctx8k on RTX 3090 (24GB) + AMD EPYC 7763 64-Core Processor (128c) + 1008GB

MeasuredOn 2026-10-22, Flint-40B-A2.5B-UD-cpumoe6-ctx8k Q4_K_XL ran at 16.8 tok/s (chat-short) on an RTX 3090 (24GB) with llama.cpp. Run r_klxxsjjp2m-.

Workload results

WorkloadBackendModeldecode tok/sprefill tok/sTTFTp50p95
chat-shortllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-cpumoe6-ctx8kQ4_K_XL16.84tok/s200.5tok/s489ms85.2ms101ms
chat-longllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-cpumoe6-ctx8kQ4_K_XL17.38tok/s1,316.2tok/s2,290ms83.9ms100ms
concurrent-decodellama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-cpumoe6-ctx8kQ4_K_XL16.80tok/s——83.9ms103ms
agent-tracellama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Flint-40B-A2.5B-UD-cpumoe6-ctx8kQ4_K_XL17.04tok/s2,973.7tok/s701ms83.8ms101ms

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: 16.8 tok/s on RTX 3090 (24GB) (Flint-40B-A2.5B-UD-cpumoe6-ctx8k)
https://llm-speed.com/r/r_klxxsjjp2m-

Related benchmarks

Provenance

Run ID
r_klxxsjjp2m-
Fingerprint hash
Public key
vyvF6hhajUdcyQtZMUo2iZqEmWvA76skTtTcz3L947w=
Received
2026-10-22 01:29:22