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Uniform INT8 is a choice to test, not a safe default for recurrent states in linear-attention or hybrid language models. Quantization error can feed into later state updates, and recent studies report accuracy costs that vary by model and task. They also show that selective-precision alternatives may preserve accuracy while reducing state memory—but their results are specific to the tested models, benchmarks, and serving setups.
Why recurrent-state quantization needs its own accuracy test
Some hybrid language models combine softmax-attention layers, which retain a growing key-value (KV) cache, with linear-attention components such as Gated DeltaNet (GDN) or Kimi Delta Attention (KDA). These components summarize prior tokens in fixed-size recurrent states. The states do not grow like a KV cache, but they can still consume substantial serving memory at high concurrency.
During decoding, a recurrent state is read and updated repeatedly. Quantizing it can reduce storage and memory traffic, but the quantized state also becomes input to later updates. The DAMP authors describe this directly: “Quantization error therefore enters subsequent updates and propagates through the recurrence, as formalized in Section 3.3.” That is why accuracy measured after a single update, or on one task, may not tell you how a format will behave over a long generation.
This concern applies to the recurrent-state structures studied in linear-attention and Delta-rule models. It should not be generalized automatically to ordinary transformer KV caches, all recurrent neural networks, or every quantization method.
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What recent recurrent-state quantization studies report
DAMP: retain selected key channels in FP16
DAMP, or Decay-Aware Mixed-Precision Recurrent-State Quantization, is a post-training approach for GDN and KDA states. Offline calibration ranks key channels by quantization risk, taking error and decay-based error retention into account. Selected high-risk channels stay in FP16; the rest use INT8 with stochastic rounding. The paper’s main configuration keeps 16 key channels per head in FP16 and reports an effective 9.9 bits per state value. See the DAMP experimental report.
In its experiments on Qwen3.6-35B, Kimi-Linear-48B, and Kimi-K3, the DAMP v2 authors report that uniform INT8 and FP8 degraded complex-reasoning accuracy, while tested INT4 and NVFP4 configurations caused severe degradation. The results varied sharply by task: INT8 with stochastic rounding was within 0.1 percentage points of FP32 on GPQA-Diamond and MMLU-Pro for Qwen3.6-35B, but accuracy fell by more than 20 percentage points on AIME 2026 and LiveCodeBench-v6. Those are results for the paper’s models, settings, and benchmarks—not a general ranking of formats.
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At its 9.9-bit configuration, DAMP reports average accuracy close to FP32 across its three evaluated checkpoints. In SGLang experiments, the authors report a 69.1% reduction in recurrent-state storage, up to 2.59× speedup for the recurrent-state update kernel, and up to 19.0% lower full-model time per output token (TPOT), each relative to FP32-state inference. At batch size 256, the reported TPOT reductions were 19.0% on Qwen3.6, 14.5% on Kimi-Linear, and 7.3% on Kimi-K3; the authors note inter-device communication as a possible contributor to the smaller Kimi-K3 reduction. In a separate multi-turn Kimi-K3 setting, they report mean time to first token 20.7% lower than with FP32 and 14.5% lower than with BF16. These figures describe the paper’s setup and should not be assumed for other hardware or workloads.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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STEPQuant: allocate precision by error and state lifetime
STEPQuant allocates precision using error magnitude and memory lifetime, then fits key-row and value-column scales using state distributions and estimated key-row impact on output error. In its report, the authors evaluate Qwen3.8-27B and Kimi-Linear-48B-A3B-Instruct. They say the nominal 6-bit setting closely matches FP32-state accuracy on tested benchmarks, and that their 4-bit configuration outperforms uniform INT8 in their experiments. See the STEPQuant experimental report.
With optimized GPU kernels integrated into SGLang, STEPQuant reports more than 5× recurrent-state compression at nominal 6 bits and up to 68.7% lower total serving memory. In one Qwen serving measurement, packed pages used 28.609 MiB per request versus 144 MiB for FP32, a 5.03× storage reduction. These measurements belong to STEPQuant’s implementation and configuration; they are not directly comparable to DAMP’s results as if both papers used the same models and benchmarks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide whether uniform INT8 fits your workload
Do not decide from the bit width alone. Compare the uniform-INT8 baseline with a suitable selective-precision option on the model and serving stack you intend to deploy. Keep task accuracy and serving performance separate: a faster update kernel does not guarantee an equally large reduction in end-to-end latency.
- Test the tasks that matter. Include the reasoning, code, or other workloads your deployment actually serves, and measure accuracy after realistic generation lengths. DAMP’s benchmark results show why a result on one task cannot stand in for another.
- Measure the complete state footprint. Count packed codes, scales, precision boundaries or maps, and retained checkpoints or cache state. Nominal bits per value do not necessarily equal total memory per request.
- Measure both update and end-to-end latency. Record recurrent-update kernel time and full-model TPOT on the target serving path. Include the intended batch size, concurrency, context and generation lengths, and tensor-parallel configuration.
- Match the method to the state geometry. GDN, KDA, and Delta-rule states should not be treated as interchangeable without validation. Confirm that the quantizer and update kernels support the architecture and state layout you use.
- Account for implementation cost. Selective methods can require offline calibration, precision maps or layouts, and compatible quantized state-update kernels. Compare that operational work with measured memory, accuracy, and latency benefits on your stack.
What the evidence does—and does not—establish
DAMP and STEPQuant are recent arXiv preprints reporting experiments on particular architectures and configurations. Their findings support testing uniform INT8 rather than adopting it automatically, and they provide selective-precision approaches to evaluate. They do not prove that INT8 is always unsuitable, that either method is best for every architecture, or that reported gains transfer unchanged to production. The available reports do not establish independent production-wide replication or population-level adoption statistics.
For publication details, consult the DAMP bibliographic record and the STEPQuant bibliographic record.
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