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Qwen3.8-Flash-Next · Q4_K_M

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NVIDIA RTX 509032GBllama.cpp b4200L0 Self-reported

This page aggregates 1 real-world runs of Qwen3.8-Flash-Next (Q4_K_M) on NVIDIA RTX 5090 with llama.cpp, contributed by 1 independent source platforms; metrics are averages of published measurements.

12.8 tok/s

Decode

Decode speed

96 tok/s

Prefill

Prefill speed

1.04 s

TTFT

Time to first token

32 GB

VRAM

VRAM usage

L0 Self-reported

1

Measured runs

1

Independent sources

Other

Source platforms

Today

Last verified

Performance

  1. Strata · Q4_K_M Decode 16.4 · Prefill 123 ·
  2. Strata · IQ3_XXS Decode 28.4 · Prefill 213 ·
  3. Strata · IQ3_S Decode 24.6 · Prefill 184.5 ·
  4. Strata · IQ4_XS Decode 18.4 · Prefill 138 ·
  5. llama.cpp · Q4_K_M (current)Decode 12.8 · Prefill 96 ·
  6. llama.cpp · IQ3_XXS Decode 22.1 · Prefill 165.8 ·
  7. llama.cpp · IQ3_S Decode 19.2 · Prefill 144 ·
  8. llama.cpp · IQ4_XS Decode 14.4 · Prefill 108 ·
  9. vLLM · IQ3_S Decode 20.9 · Prefill 156.8 ·

Core figures

Decode (avg)
12.8 tok/s
Prefill (avg)
96 tok/s
TTFT (avg)
1.04 s
VRAM (avg)
32 GB
MTP acceptance rate
—
TTFB
— GB
Power draw
— W

Configuration

Member-level fields are taken from the most recent run

Model
Qwen3.8-Flash-Next
Quantization
Q4_K_M
Framework
llama.cpp
Version
b4200
Context length
4096 tokens
Batch size
512
GPU layers
99
Flash Attention
On

Hardware

Nominal and measured figures are shown side by side; whether it runs is the reader's call

GPU
NVIDIA RTX 5090
Nominal VRAM
32 GB
Measured VRAM (avg)
32 GB
OS
Linux
Driver
570.65
CUDA
12.6

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Original model resources

Base model resources may not match this quantization. Use the verified deployment weights above when available.

Sources & evidence

1 measured records in total, each traceable to its original source

  1. L0 Self-reportedOtherOriginal link Verified on 2026-10-06

    b4200 · Linux · CUDA 12.6 · 4096 ctx

    12.8 tok/s

    Decode

    96 tok/s

    Prefill

    1.04 s

    TTFT

    32 GB

    VRAM

    —

    MTP

    — W

    Power draw

    L0 理论估算(add-1000-records-batch3)