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llm-speed

Qwen3.8-27B-filled16k on RTX 4090 (24GB)

MeasuredOn 2026-10-29, Qwen3.8-27B-filled16k Q4_K_M ran at 41.2 tok/s (long-context-decay) on an RTX 4090 (24GB) with llama.cpp. Run r_f0etksoa6-y.

Model weights:View on Hugging Faceggml-org/Qwen3.8-27B-GGUFQwen3.8-27B-Q4_K_M.gguf

RTX 4090 (24GB)RTX 4090 (24GB)
suite suite-v1
cli 0.0.9
signedthR+FI7rfB…
Embed badge@llmspeedsubmitted Oct 29, 2026

Workload results

WorkloadBackendModeldecode tok/sprefill tok/sTTFTp50p95
long-context-decayllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Qwen3.8-27B-filled16kQ4_K_M41.21tok/s2,978.7tok/s5,635ms24.1ms24.5ms

Long-context results

Qwen3.8-27B-filled16k · llama.cpp. Each row is a point this run measured, keyed on the prompt tokens the runtime counted (the depth of context the prompt filled).

Prompt tokens are not the configured context capacity. “—” means unmeasured or unavailable.

Scroll the table sideways for all measurements.

Long-context measurements and status for each measured prompt length.
Actual prompt tokensFirst token (s)Decode tok/sOutput tokensStatus
16,7865.63541.21256measured

First-token delay includes processing the prompt with the model loaded. These speed measurements do not test answer quality or the maximum usable context.

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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.

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llm-speed: 41.2 tok/s on RTX 4090 (24GB) (Qwen3.8-27B-filled16k)
https://llm-speed.com/r/r_f0etksoa6-y

Related benchmarks

Provenance

Run ID
r_f0etksoa6-y
Fingerprint hash
Public key
thR+FI7rfBWhr5krguKQFfmmiuZ0fOOKRbfpEC/gtTE=
Received
2026-10-29 13:19:14