Acceleration Methods

LOOM-Eval provides cutting-edge acceleration techniques optimized for long-context processing. All methods support multi-GPU inference and are compatible with NVIDIA 3090, A-series, and H-series GPUs.

KV Cache Optimization

Method	Description	Source
H2O	Attention-based KV cache selection	GitHub
StreamingLLM	Retains initial tokens in cache	GitHub
SnapKV	Attention pooling before selection	GitHub
L2Compress	Uses L2 norm for cache compression	GitHub
PyramidKV	Layer-wise budget allocation	GitHub
CakeKV	Layer-specific preference scoring	GitHub
KIVI	2bit asymmetric quantization	GitHub
ThinK	Query-driven key cache pruning	GitHub
CaM	Cache merging for memory efficiency	GitHub

Sparse Attention

Method	Description	Source
FlexPrefill	Dynamic context-aware sparse attention	GitHub
XAttention	Strided antidiagonal scoring (A100-optimized)	GitHub

Usage

Enable acceleration via CLI:

loom-eval.run \
    --model_path Meta-Llama-3.1-8B-Instruct \
    --cfg_path ./benchmarks/General/RULER.yaml \
    --acceleration SnapKV \
    --device 0 1 \
    --gp_num 2 \
    --eval \

Performance (128K Context)

Results for Llama-3.1-8B-Instruct on RULER benchmark (40GB A100):

Method	Score	Time
KIVI	86.48	0:32:57
H2O	22.98	0:27:00
StreamingLLM	15.02	0:26:35
L2Compress	23.16	0:27:26
CaM	46.68
CakeKV	74.47	0:28:26
PyramidKV	64.28	0:26:39
FlexPrefill	76.50	0:15:58
SnapKV	64.72	0:26:44
ThinK	66.45	0:35:46
Transformers	75.21	3:23:10
vLLM	76.11	0:27:20
SGLang	82.77	0:20:27
XAttention	78.58	0:16:34

Model Compatibility

Method	llama	mistral	glm	qwen2
KV Optimization	✓	✓	✗	✗
FlexPrefill	✓	✗	✓	✓
CakeKV	✓	✓	✗	✓
XAttention	✓	✗	✗	✗

Installation Notes

KIVI::

cd KIVI/quant && pip install -e .

ThinK::

cd think/load_tiny_cuda_resource && make i

FlexPrefill::

cd flex_prefil && pip install -e .

XAttention::

cd x-attention && pip install -e .

Hardware Requirements

• 128K context support on single 40GB A100 (all methods except L2Compress)

• Multi-GPU recommended for 24GB cards

• XAttention only support A100 optimization