Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →HyQuant is a research method that keeps selected attention positions and recent context in full precision while storing or computing most other attention states in low-bit formats. Its authors report decode-kernel speedups of up to 3.58× over FlashAttention-2 on tested context lengths, but end-to-end decode gains were smaller—1.04× to 1.17×—and benchmark accuracy stayed close to the full-precision baseline in the reported tests. These are results on a bounded set of models, tasks, and hardware, not a general production guarantee.
How HyQuant allocates precision
HyQuant is built around the observation that attention is not distributed evenly across key positions: some positions attract attention persistently across queries. The method identifies these important “vertical-line” positions, keeps them in full precision, and also protects a recent local window. Other context positions are handled in lower precision.
The authors’ analysis of Llama-3.1-8B and Qwen3-8B found that the top 5% of key positions together with a 128-token local window covered 85.63% and 82.53% of attention mass, respectively. Those measurements describe these two models and the authors’ analysis; they should not be assumed to hold for other models.
During prefill
For prefill, HyQuant computes selected vertical-line positions and the local sliding window in full precision, while computing the remaining context in low precision.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
#1 Best Overall
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
During decode
For decode, most keys and values in the KV cache are stored in 4-bit formats, while selected positions are retained in full precision. HyQuant fuses dequantization with attention rather than first expanding the entire cache into a full-precision copy. The paper’s tested implementation uses Key-4bit and Value-4bit for the remaining KV positions.
What the reported accuracy evidence shows
The paper evaluates Qwen3-8B, Qwen3-32B, Llama-3.1-8B-Instruct, and GLM-4-9B-0414. It reports LongBench v1 results for long-context tasks, plus GSM8K and MATH500 results for mathematical reasoning.
Rank #2
- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
On the Qwen3-8B thinking-mode LongBench v1 table, HyQuant’s reported average across 11 tasks is 45.04, versus 44.59 for the full-precision FlashAttention-2 baseline. On Llama-3.1-8B-Instruct, the reported averages are 46.73 and 46.63, respectively. These small differences are measured benchmark outcomes, not evidence that quantization makes the underlying model more capable; the authors characterize small gains of this kind as normal evaluation variance.
Operator error is not the same as task accuracy
In a separate Qwen3-8B analysis, the authors compare intermediate attention-output mean squared error with full-precision FlashAttention. Keeping the top 1% or 5% of high-score positions in full precision and quantizing the rest to 4-bit brought measured error toward the uniform 8-bit error level across tested sequence lengths from 1K to 32K. This is evidence about attention-operator error, not a guarantee of downstream task scores.
Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWindows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallRank #3
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
Speedups: kernel results versus end-to-end decoding
On an NVIDIA H100, the authors report the following decode speedups relative to FlashAttention-2. Kernel-level improvements grow with prefix length, while end-to-end gains remain much smaller:
| Prefix length | Decode-kernel speedup | End-to-end decode speedup |
|---|---|---|
| 1,024 tokens | 1.32× | 1.04× to 1.17× across tested prefix lengths; exact per-length value not stated in the cited summary (HyQuant authors, 2026) |
| 2,048 tokens | 2.40× | 1.04× to 1.17× across tested prefix lengths; exact per-length value not stated in the cited summary (HyQuant authors, 2026) |
| 4,096 tokens | 3.06× | 1.04× to 1.17× across tested prefix lengths; exact per-length value not stated in the cited summary (HyQuant authors, 2026) |
| 8,192 tokens | 3.36× | 1.04× to 1.17× across tested prefix lengths; exact per-length value not stated in the cited summary (HyQuant authors, 2026) |
| 16,384 tokens | 3.52× | 1.04× to 1.17× across tested prefix lengths; exact per-length value not stated in the cited summary (HyQuant authors, 2026) |
| 32,768 tokens | 3.58× | 1.04× to 1.17× across tested prefix lengths; exact per-length value not stated in the cited summary (HyQuant authors, 2026) |
The kernel figures isolate the decode kernel; end-to-end decoding includes more of the inference path, so its improvement is the more relevant number for estimating overall serving impact. The reported range does not establish how a particular serving stack will perform.
Rank #4
- 48GB AI graphics accelerator
Memory and runtime trade-offs
- Position-selection overhead: The authors attribute 3%–5% of total runtime to identifying vertical-line positions.
- Extra cache for retained precision: At the reported 5% retention setting, keeping vertical-line tokens in full precision increases non-window KV-cache size by about 15% versus strict 4-bit quantization. The total cache cost also depends on local-window size.
- Accuracy versus memory: Increasing the retained-token ratio generally lowers quantization error and improves accuracy in the authors’ tests, but increases the high-precision memory budget. A larger full-precision window also slightly improved accuracy in the reported ablation.
How far the findings generalize
The reported evidence covers the four named models, the listed benchmark families, the tested context lengths, and experiments on an NVIDIA H100. It does not establish independent replication, compatibility across serving systems, or performance for every model architecture and workload. A like-for-like evaluation should keep the model, context length, low-bit format, retained-position ratio, local-window size, hardware, and latency measurement boundary consistent; kernel latency alone is not an end-to-end deployment comparison.
The paper’s current arXiv record lists an initial submission on 28 August 2026, version 3 revised on 16 September 2026, and the comment “EMNLP 2026 Main.” The record links to the authors’ implementation at arXiv:2608.22153 and the HyQuant GitHub repository.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsQuick Recap
Best Value
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
