[Feature]: Prefix caching completely ineffective for Mamba-hybrid models (Qwen3.5) when prompt < block_size (528 tokens)

[Gaodzlearn]

🚀 The feature, motivation and pitch

Environment (Brief)

• vLLM version: 0.19.1
• GPU: NVIDIA A100/H20
• Model: Qwen/Qwen3.5-4B (Mamba-hybrid architecture)

Description

When serving Mamba-hybrid models like Qwen3.5, vLLM sets the attention block size to 528 tokens to align with the mamba page size:

INFO [config.py:281] Setting attention block size to 528 tokens to ensure that attention page size is >= mamba page size.

Since prefix caching operates at the block granularity, only fully completed blocks are cached. This means:

• Prompt with < 528 tokens: 0% prefix cache hit — the entire prefix must be recomputed every request
• Prompt with 552 tokens: ~95% hit (1 block cached, only 24 tokens recomputed)
• Prompt with 979 tokens: ~54% hit (1 block cached, 451 tokens recomputed)

For comparison, standard transformer models use block_size=16, so even a 100-token prompt gets 6 fully cached blocks (96 tokens cached).

Reproduction

# Start vllm serve with prefix caching
vllm serve Qwen/Qwen3.5-4B \
  --enable-prefix-caching \
  --max-model-len 1024 \
  --dtype bfloat16

# Send requests with the same ~480 token prefix but different query suffixes
# Observe from vllm stats log:
# "Prefix cache hit rate: 0.0%"  (when total prompt < 528 tokens)

Measured results from our benchmark (200 requests each, same prefix, different short suffix):

Prompt length	Full blocks	Prefix cache hit rate
479 tokens	0	~0%
552 tokens	1	95.4%
597 tokens	1	88.2%
979 tokens	1	53.7%

Impact

This is a severe performance cliff for short-prompt, high-QPS workloads common in production (classification, intent detection, routing, etc.). These tasks typically have prompts of 300-600 tokens. Users enabling --enable-prefix-caching expect it to help, but it silently does nothing — or even hurts performance due to the caching overhead.

In our case, QPS dropped from 200 to <100 when the prompt was shortened from ~560 to ~480 tokens, purely because it crossed below the 528-token block boundary.

Suggested fix

For Mamba-hybrid models with large block sizes, consider one of:

1. Sub-block prefix caching: Allow caching at a finer granularity (e.g., 16-token chunks within the 528-token block), even if the mamba state requires full-block alignment for its own cache
2. Automatic padding: Pad the prefix to the next block boundary before caching, so that short prompts can still benefit
3. Warn the user: At minimum, log a warning when enable_prefix_caching=True and the block size is unusually large (>64), telling the user the minimum prompt length needed to benefit
4. Decouple attention and mamba block sizes: Use separate block sizes for the attention KV cache (small, e.g. 16) and the mamba state cache (large, 528), so prefix caching for attention layers is not penalized by mamba alignment requirements

Option 4 seems the most correct — there's no fundamental reason the attention KV cache block size needs to match the mamba page size for prefix caching purposes.

Environment (Detail)：

==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version                : Could not collect
CMake version                : version 4.1.0
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.13.13 | packaged by Anaconda, Inc. | (main, Apr 14 2026, 06:19:41) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-5.4.119-19.0009.56-x86_64-with-glibc2.35
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.8.93
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20

Nvidia driver version        : 535.216.01
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.14.0
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   52 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          384
On-line CPU(s) list:             0-383
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 9K84 96-Core Processor
CPU family:                      25
Model:                           17
Thread(s) per core:              2
Core(s) per socket:              96
Socket(s):                       2
Stepping:                        0
BogoMIPS:                        5200.10
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ibpb vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 avx512_bf16 clzero xsaveerptr wbnoinvd arat avx512vbmi umip avx512_vbmi2 vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       6 MiB (192 instances)
L1i cache:                       6 MiB (192 instances)
L2 cache:                        192 MiB (192 instances)
L3 cache:                        768 MiB (24 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-191
NUMA node1 CPU(s):               192-383
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Full AMD retpoline, IBPB conditional, STIBP disabled, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==5.5.4
[pip3] triton==3.6.0
[conda] flashinfer-python                           0.6.6            pypi_0              pypi
[conda] numpy                                       2.2.6            pypi_0              pypi
[conda] nvidia-cublas-cu12                          12.8.4.1         pypi_0              pypi
[conda] nvidia-cuda-cupti-cu12                      12.8.90          pypi_0              pypi
[conda] nvidia-cuda-nvrtc-cu12                      12.8.93          pypi_0              pypi
[conda] nvidia-cuda-runtime-cu12                    12.8.90          pypi_0              pypi
[conda] nvidia-cudnn-cu12                           9.10.2.21        pypi_0              pypi
[conda] nvidia-cudnn-frontend                       1.18.0           pypi_0              pypi
[conda] nvidia-cufft-cu12                           11.3.3.83        pypi_0              pypi
[conda] nvidia-cufile-cu12                          1.13.1.3         pypi_0              pypi
[conda] nvidia-curand-cu12                          10.3.9.90        pypi_0              pypi
[conda] nvidia-cusolver-cu12                        11.7.3.90        pypi_0              pypi
[conda] nvidia-cusparse-cu12                        12.5.8.93        pypi_0              pypi
[conda] nvidia-cusparselt-cu12                      0.7.1            pypi_0              pypi
[conda] nvidia-cutlass-dsl                          4.4.2            pypi_0              pypi
[conda] nvidia-cutlass-dsl-libs-base                4.4.2            pypi_0              pypi
[conda] nvidia-ml-py                                13.595.45        pypi_0              pypi
[conda] nvidia-nccl-cu12                            2.27.5           pypi_0              pypi
[conda] nvidia-nvjitlink-cu12                       12.8.93          pypi_0              pypi
[conda] nvidia-nvshmem-cu12                         3.4.5            pypi_0              pypi
[conda] nvidia-nvtx-cu12                            12.8.90          pypi_0              pypi
[conda] pyzmq                                       27.1.0           pypi_0              pypi
[conda] torch                                       2.10.0           pypi_0              pypi
[conda] torch-c-dlpack-ext                          0.1.5            pypi_0              pypi
[conda] torchaudio                                  2.10.0           pypi_0              pypi
[conda] torchvision                                 0.25.0           pypi_0              pypi
[conda] transformers                                5.5.4            pypi_0              pypi
[conda] triton                                      3.6.0            pypi_0              pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.19.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    0-191   0               N/A
GPU1    NV18     X      0-191   0               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=none
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.8.1
LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:/usr/local/mpi/lib:/usr/local/cuda/lib64
OMP_NUM_THREADS=1
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root```

### Alternatives

_No response_

### Additional context

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[ZJY0516]

Thanks for feedback, this is a limitation of current vllm hybrid model prefix caching design

[skycore9]

Thanks for feedback, this is a limitation of current vllm hybrid model prefix caching design

Any progress?

[ZJY0516]

see this: #45702