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@@ -205,14 +205,20 @@ We report the following numbers with 1K prefill and 100 decode tokens:
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  | Device | Inference | Framework | Model | Prefill (tok/s) | Decode (tok/s) | Memory |
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  | ---------------------------------------------------- | --------- | ---------------- | -------------------- | --------------- | -------------- | ------ |
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- | AMD Ryzen AI 395+ | NPU | FastFlowLM | LFM2.5-1.2B-Thinking | 1373 | 60 | 1700MB |
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- | AMD Ryzen AI 9 HX 370 | NPU | FastFlowLM | LFM2.5-1.2B-Thinking | 1256 | 57 | 1700MB |
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- | AMD Ryzen AI 9 HX 370 | CPU | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 2975 | 116 | 856MB |
 
 
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  | Qualcomm Snapdragon® X Elite | NPU | NexaML | LFM2.5-1.2B-Thinking | 2591 | 63 | 0.9GB |
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  | Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro) | NPU | NexaML | LFM2.5-1.2B-Thinking | 4391 | 82 | 0.9GB |
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  | Qualcomm Dragonwing IQ9 (IQ-9075) (IoT) | NPU | NexaML | LFM2.5-1.2B-Thinking | 2143 | 53 | 0.9 GB |
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  | Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 335 | 70 | 719MB |
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  These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.
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  ## Contact
 
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  | Device | Inference | Framework | Model | Prefill (tok/s) | Decode (tok/s) | Memory |
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  | ---------------------------------------------------- | --------- | ---------------- | -------------------- | --------------- | -------------- | ------ |
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+ | AMD Ryzen AI 395+ | NPU | FastFlowLM | LFM2.5-1.2B-Thinking | 1487 | 60 | 1700MB |
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+ | AMD Ryzen AI 5 HX 340 | NPU | FastFlowLM | LFM2.5-1.2B-Thinking | 1431 | 63 | 1700MB |
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+ | AMD Ryzen AI 7 HX 350 | NPU | FastFlowLM | LFM2.5-1.2B-Thinking | 1431 | 63 | 1700MB |
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+ | AMD Ryzen AI 9 HX 370 | NPU | FastFlowLM | LFM2.5-1.2B-Thinking | 1487 | 57 | 1700MB |
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+ | AMD Ryzen AI 9 HX 370 | GPU | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 2975 | 116 | 856MB |
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  | Qualcomm Snapdragon® X Elite | NPU | NexaML | LFM2.5-1.2B-Thinking | 2591 | 63 | 0.9GB |
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  | Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro) | NPU | NexaML | LFM2.5-1.2B-Thinking | 4391 | 82 | 0.9GB |
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  | Qualcomm Dragonwing IQ9 (IQ-9075) (IoT) | NPU | NexaML | LFM2.5-1.2B-Thinking | 2143 | 53 | 0.9 GB |
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  | Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 335 | 70 | 719MB |
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+ LFM2.5-1.2B-Thinking works very well with long contexts on AMD NPU. It delivers **~59 tok/s at 4K** context and **~52 tok/s at 16K** during decoding, and reaches a **prefill speed of ~2,226 tok/s with a 4K-token prompt**.
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+ Check the detailed benchmark results [here](https://fastflowlm.com/docs/benchmarks/lfm2_results/).
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  These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.
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  ## Contact