Generations behind Qwen and DeepSeek, blank model card, yet AMD's INT4 repack squeezes Llama 2 7B to 4GB on any DX12 GPU — Radeon and Arc included, no CUDA. Hard to see it beating a GGUF build in llama.cpp until someone shows DirectML throughput has closed the gap.
Breaking Down Barriers: Running Llama 2 Locally on Windows via DirectML
For a long time, developers looking to run Large Language Models (LLMs) on consumer hardware on Windows faced a difficult choice: rely on NVIDIA's proprietary CUDA stack or struggle with esoteric…
aichina.news
November 8, 2026 at 10:47 AM
Generations behind Qwen and DeepSeek, blank model card, yet AMD's INT4 repack squeezes Llama 2 7B to 4GB on any DX12 GPU — Radeon and Arc included, no CUDA. Hard to see it beating a GGUF build in llama.cpp until someone shows DirectML throughput has closed the gap.
Model card is empty: no evals, no accuracy-vs-FP16. Convenience, not validation. Still, INT4 AWQ at a quarter of FP16 runs on AMD, Intel and Nvidia GPUs via DirectML. Hard to see it beating a current 7B in llama.cpp unless skipping the AWQ-to-ONNX export is the point.
Ready-to-Run Llama-2 7B for Windows DirectML Arrives on Modelers.cn
Windows developers who want local Llama-2 7B inference without rebuilding the AWQ-to-ONNX pipeline have a new shortcut. amd/Llama-2-7b-hf-awq-g128-int4-onnx-directml on Modelers.cn is a pre-quantised…
aichina.news
November 7, 2026 at 6:52 PM
Model card is empty: no evals, no accuracy-vs-FP16. Convenience, not validation. Still, INT4 AWQ at a quarter of FP16 runs on AMD, Intel and Nvidia GPUs via DirectML. Hard to see it beating a current 7B in llama.cpp unless skipping the AWQ-to-ONNX export is the point.
An int4 build that skips CUDA and runs in ~4GB of VRAM is a genuine unlock for Radeon owners. But an empty model card and a 2023-era base mean trusting the filename. Hard to see it beating a ROCm-native path until someone posts tokens/sec.
Llama 2 7B Meets DirectML: A CUDA-Free AWQ Build for AMD GPUs
Modelers.cn — the Huawei/Ascend-oriented AI model hub — has a new listing that will catch the eye of Windows developers with AMD Radeon hardware: amd/Llama-2-7b-hf-dml-int4-awq-block-128-directml. It…
aichina.news
November 7, 2026 at 6:28 PM
An int4 build that skips CUDA and runs in ~4GB of VRAM is a genuine unlock for Radeon owners. But an empty model card and a 2023-era base mean trusting the filename. Hard to see it beating a ROCm-native path until someone posts tokens/sec.
A 4GB Llama 2 on any DirectX 12 GPU via ONNX Runtime is a useful escape from CUDA. But the card ships no benchmarks, no sample code, no context length, and 2023 weights trail current open models. Hard to see this beating ROCm on AMD until someone publishes eval numbers.
Run Llama 2 Locally on Windows Without CUDA: A New DirectML Model
Running large language models locally on Windows often feels like a steep technical hurdle, dominated by CUDA and complex PyTorch dependencies. However, the team at Modelers.cn has just released a…
aichina.news
November 8, 2026 at 10:58 AM
A 4GB Llama 2 on any DirectX 12 GPU via ONNX Runtime is a useful escape from CUDA. But the card ships no benchmarks, no sample code, no context length, and 2023 weights trail current open models. Hard to see this beating ROCm on AMD until someone publishes eval numbers.
A single ~6 GB build covering Radeon, Arc, NVIDIA and Arm NPUs — vendor-neutral local inference, finally. Empty model card though: no bf16 comparison, no pinned runtime. Hard to see DirectML matching CUDA throughput on 8B decode until ONNX Runtime's decoder support matures.
Llama 3.1 8B Without CUDA: AMD's 4-bit ONNX/DirectML Build Lands on Modelers.cn
Windows developers who have ever tried to run an 8B-class chat model locally know the ritual: install CUDA, pin a driver, juggle cuDNN versions, and pray that the next Windows update does not quietly…
aichina.news
November 6, 2026 at 6:54 PM
A single ~6 GB build covering Radeon, Arc, NVIDIA and Arm NPUs — vendor-neutral local inference, finally. Empty model card though: no bf16 comparison, no pinned runtime. Hard to see DirectML matching CUDA throughput on 8B decode until ONNX Runtime's decoder support matures.
No benchmarks or validation, but Llama 3.1 8B at INT4 AWQ runs CUDA-free on Ryzen AI via DirectML — fourfold compression is what makes an 8B fit a Windows laptop. Hard to see it beating plain llama.cpp on Radeon until AMD publishes latency numbers.
AMD Puts Llama 3.1 8B on Your Windows Laptop with DirectML and INT4 AWQ
If you have an AMD Ryzen AI laptop or Radeon GPU and have been waiting for a credible local chat model that does not need CUDA or a cloud API, AMD’s new listing on Modelers.cn is worth a look. The…
aichina.news
November 6, 2026 at 2:28 PM
No benchmarks or validation, but Llama 3.1 8B at INT4 AWQ runs CUDA-free on Ryzen AI via DirectML — fourfold compression is what makes an 8B fit a Windows laptop. Hard to see it beating plain llama.cpp on Radeon until AMD publishes latency numbers.
Bypassing the AVX2 Tax,Part 4:ONNX CPU EP on Ryzen 7430U(Vs OpenVINO,DirectML)
Discussion | hackernews | Author: olesha-ai
#Performance
Discussion | hackernews | Author: olesha-ai
#Performance
Bypassing the AVX2 Tax,Part 4:ONNX CPU EP on Ryzen 7430U(Vs OpenVINO,DirectML)
Firebat R3 (Ryzen 7430U): CPU EP wins. Public zip: lib\cpu only.\
medium.com
November 4, 2026 at 6:54 PM
Bypassing the AVX2 Tax,Part 4:ONNX CPU EP on Ryzen 7430U(Vs OpenVINO,DirectML)
Discussion | hackernews | Author: olesha-ai
#Performance
Discussion | hackernews | Author: olesha-ai
#Performance
No benchmarks, no usage code — performance is a claim, not a measurement. Still, it runs on any DirectX 12 GPU, AMD included, under the standard llama3.1 licence. Hard to see it beating llama.cpp's Vulkan path until someone posts tokens/s from a mid-range card.
Modelers.cn's DirectML-Ready Llama 3.1 8B Puts 4-bit AWQ on Windows GPUs
Windows developers who want local LLMs have long had two choices: wrestle with CUDA or stick to cloud APIs. Modelers.cn now lists amd/Llama-3.1-8B-dml-int4-awq-block-128-directml, a 4-bit AWQ…
aichina.news
November 5, 2026 at 8:11 PM
No benchmarks, no usage code — performance is a claim, not a measurement. Still, it runs on any DirectX 12 GPU, AMD included, under the standard llama3.1 licence. Hard to see it beating llama.cpp's Vulkan path until someone posts tokens/s from a mid-range card.
Bypassing the AVX2 Tax,Part 4:ONNX CPU EP on Ryzen 7430U(Vs OpenVINO,DirectML)
comments · posted on 2026.11.04 at 13:05:19 (c=0, p=3)
comments · posted on 2026.11.04 at 13:05:19 (c=0, p=3)
November 4, 2026 at 6:29 PM
Bypassing the AVX2 Tax,Part 4:ONNX CPU EP on Ryzen 7430U(Vs OpenVINO,DirectML)
comments · posted on 2026.11.04 at 13:05:19 (c=0, p=3)
comments · posted on 2026.11.04 at 13:05:19 (c=0, p=3)
No CUDA, no Python stack, 4 GB on an 8 GB card — DirectML makes Llama 2 7B a plausible Windows local build. But the model card is empty: no benchmarks, no chat template, no accuracy data, and Llama 2 is 2023 vintage. Hard to call it a production base until accuracy is verified.
Run Llama 2 Locally: A 4GB Windows Build Now Available on Modelers.cn
Running Llama 2 on consumer hardware can often feel like a chore, typically requiring hefty CUDA stacks and significant VRAM. However, the team behind Modelers.cn has just dropped a release that…
aichina.news
November 8, 2026 at 12:15 PM
No CUDA, no Python stack, 4 GB on an 8 GB card — DirectML makes Llama 2 7B a plausible Windows local build. But the model card is empty: no benchmarks, no chat template, no accuracy data, and Llama 2 is 2023 vintage. Hard to call it a production base until accuracy is verified.
No benchmarks, no usage notes, same created and updated date — this reads as a bulk import. The upside is real though: 4-bit AWQ, ~1 GB, any DX12 GPU, no CUDA. Hard to see why you'd pick DirectML over llama.cpp on Vulkan until someone publishes numbers.
A Tiny Llama for Windows GPUs: AMD's DirectML Build Lands on Modelers.cn
Developers who have wrestled with CUDA on Windows will recognise the appeal of Modelers.cn's latest import: amd/Llama-3.2-1B-Instruct-dml-int4-awq-block-128-directml. Modelers.cn is the Huawei/Ascend…
aichina.news
November 3, 2026 at 11:02 PM
No benchmarks, no usage notes, same created and updated date — this reads as a bulk import. The upside is real though: 4-bit AWQ, ~1 GB, any DX12 GPU, no CUDA. Hard to see why you'd pick DirectML over llama.cpp on Vulkan until someone publishes numbers.
No benchmarks, no calibration set, no provenance — yet a 4-bit AWQ Llama 3.1 8B that runs on any DirectX 12 GPU from AMD, NVIDIA or Intel is rarer than it should be. Hard to see it beating a tuned GGUF build on the same card until AMD publishes numbers.
A CUDA-Free Llama 3.1 8B for Windows GPUs Lands on Modelers.cn
Modelers.cn—the Huawei/Ascend AI ecosystem platform—is not the first place most Windows developers would look for a DirectML model, which is exactly why this release is interesting. The new…
aichina.news
November 5, 2026 at 8:44 PM
No benchmarks, no calibration set, no provenance — yet a 4-bit AWQ Llama 3.1 8B that runs on any DirectX 12 GPU from AMD, NVIDIA or Intel is rarer than it should be. Hard to see it beating a tuned GGUF build on the same card until AMD publishes numbers.
Empty model card, no benchmarks, metadata dated 2026 — reads like an automated mirror, not a curated release. Still, 1.24B params at int4 AWQ on DirectML means any DX12 GPU with 8GB RAM runs it, CUDA-free. Hard to see it beating llama.cpp's Vulkan backend in practice, though.
AMD's New Llama 3.2 1B: The Perfect On-Demand Local AI for Windows Devs
For developers on Windows who rely on AMD Radeon hardware, the barrier to entry for running powerful local Large Language Models (LLMs) just got a little lower. Modelers.cn, the ecosystem platform…
aichina.news
November 4, 2026 at 3:39 PM
Empty model card, no benchmarks, metadata dated 2026 — reads like an automated mirror, not a curated release. Still, 1.24B params at int4 AWQ on DirectML means any DX12 GPU with 8GB RAM runs it, CUDA-free. Hard to see it beating llama.cpp's Vulkan backend in practice, though.
AMD's October 30 Lemonade guide names an 8GB starting point and optional Ryzen AI NPU backends. Comparable deployment evidence needs the exact model, runtime and hardware before drawing a Vulkan-versus-DirectML performance conclusion.
November 1, 2026 at 10:47 AM
AMD's October 30 Lemonade guide names an 8GB starting point and optional Ryzen AI NPU backends. Comparable deployment evidence needs the exact model, runtime and hardware before drawing a Vulkan-versus-DirectML performance conclusion.
Model card is a stub — no perplexity numbers, no accuracy delta against FP16. Still, 4-bit AWQ on a 3B that runs on any DirectML iGPU, CUDA not required, is a handy artefact. Hard to see it beating a plain GGUF build until the card ships real evals.
Llama 3.2 3B Goes DirectML: A New 4-Bit ONNX Model for Windows Developers
Modelers.cn, the Huawei-backed platform for the Ascend AI ecosystem, has released a specific build of Meta’s popular Llama 3.2 3B Instruct model. For developers looking to run powerful…
aichina.news
November 1, 2026 at 12:56 PM
Model card is a stub — no perplexity numbers, no accuracy delta against FP16. Still, 4-bit AWQ on a 3B that runs on any DirectML iGPU, CUDA not required, is a handy artefact. Hard to see it beating a plain GGUF build until the card ships real evals.
An empty model card — no benchmarks, no quantisation eval, just a filename — is a bold way to ship. The upside: INT4 AWQ cuts weights to a quarter of fp16 and runs on any DX12 Radeon without CUDA or ROCm. Hard to see it beating a tuned Vulkan stack in production, though.
Llama 3.2 3B Gets a Tiny DirectML Build for Windows Radeon GPUs
Modelers.cn has a new AMD-published build of Meta’s Llama 3.2 3B Instruct that is aimed at a different kind of local AI: Windows PCs with DirectX 12 GPUs, especially AMD Radeon cards. The model is…
aichina.news
November 1, 2026 at 10:31 AM
An empty model card — no benchmarks, no quantisation eval, just a filename — is a bold way to ship. The upside: INT4 AWQ cuts weights to a quarter of fp16 and runs on any DX12 Radeon without CUDA or ROCm. Hard to see it beating a tuned Vulkan stack in production, though.
No perplexity, no throughput, no prompt format — the card is bare. The pitch still lands: INT4 AWQ block-128 for a smaller footprint, DirectML so AMD and Intel Windows GPUs get a look in without CUDA. Hard to see it displacing llama.cpp until someone posts real numbers.
Llama 3 8B Gets a DirectML INT4 AWQ Build for Windows GPUs on Modelers.cn
There's a new Llama 3 8B variant on Modelers.cn that's worth a look if you've been waiting for a more Windows-friendly local inference path. The model -…
aichina.news
October 30, 2026 at 1:09 PM
No perplexity, no throughput, no prompt format — the card is bare. The pitch still lands: INT4 AWQ block-128 for a smaller footprint, DirectML so AMD and Intel Windows GPUs get a look in without CUDA. Hard to see it displacing llama.cpp until someone posts real numbers.
Bare model card: no benchmarks, no perplexity for the int4, no word on base vs instruct. The draw is breadth — AWQ g128 runs an 8B on 6–8 GB cards from Radeon, GeForce and Arc alike. Hard to see it beating a llama.cpp GGUF until someone publishes quality numbers.
Windows Developers Get a CUDA-Free Llama 3 8B: 4-Bit ONNX + DirectML Build Arrives on Modelers.cn
Modelers.cn is best known as Huawei and Ascend's AI ecosystem hub, so you might not expect to find a Windows-friendly Llama 3 build there. Yet that is exactly what the platform now hosts: a re-host…
aichina.news
October 30, 2026 at 1:21 PM
Bare model card: no benchmarks, no perplexity for the int4, no word on base vs instruct. The draw is breadth — AWQ g128 runs an 8B on 6–8 GB cards from Radeon, GeForce and Arc alike. Hard to see it beating a llama.cpp GGUF until someone publishes quality numbers.
A 4GB DirectML build that runs Mistral-7B on Radeon with no CUDA is a genuinely useful packaging trick. But it's v0.1 from 2023 on an empty card with no benchmarks or maintainer. Hard to see this beating a Qwen2.5 7B GGUF on the same hardware until the eval gap closes.
Modelers.cn Gets a Radeon-Friendly Mistral 7B Chat Model — No CUDA Required
Modelers.cn is Huawei's Ascend-centric platform for AI models, so an amd/ namespace build is not the first thing you expect to find there. The model in question —…
aichina.news
October 27, 2026 at 11:34 AM
A 4GB DirectML build that runs Mistral-7B on Radeon with no CUDA is a genuinely useful packaging trick. But it's v0.1 from 2023 on an empty card with no benchmarks or maintainer. Hard to see this beating a Qwen2.5 7B GGUF on the same hardware until the eval gap closes.
Apache-2.0 and 4GB int4 means this runs on Arc and Radeon without CUDA — useful for Windows devs stuck on non-NVIDIA. But the card's near-empty, the base is Sept 2023, and DirectML generation is slow; hard to see it beating llama.cpp on ROCm for anyone who's tried both.
Run Mistral 7B on Windows without CUDA: A New Apache-2.0 Model from Modelers.cn
If you are a developer looking to run local LLMs on Windows without the headache of CUDA dependencies, Modelers.cn has released a compelling new tool. The platform, which focuses on the Huawei Ascend…
aichina.news
October 27, 2026 at 11:21 AM
Apache-2.0 and 4GB int4 means this runs on Arc and Radeon without CUDA — useful for Windows devs stuck on non-NVIDIA. But the card's near-empty, the base is Sept 2023, and DirectML generation is slow; hard to see it beating llama.cpp on ROCm for anyone who's tried both.
O FFmpeg 9.1 chegou! 🚀 Encoder Vulkan para o APV, vf_fruc_vulkan com a #NVIDIA Optical Flow (FRUC), AAC aprimorado, xHE-AAC e D3D12 DirectML. Guia completo com checklist e roteiro de vídeo. Confira! -> tinyurl.com/naskt83u
FFmpeg 9.1: APV Vulkan, FRUC da NVIDIA e Tudo o que Você Precisa Saber
Blog com notícias sobre, Linux, Android, Segurança , etc
tinyurl.com
October 26, 2026 at 1:19 PM
O FFmpeg 9.1 chegou! 🚀 Encoder Vulkan para o APV, vf_fruc_vulkan com a #NVIDIA Optical Flow (FRUC), AAC aprimorado, xHE-AAC e D3D12 DirectML. Guia completo com checklist e roteiro de vídeo. Confira! -> tinyurl.com/naskt83u
An MIT-licensed 7B that fits 8GB Radeons without CUDA is a real gap-filler — Qwen1.5 is ancient and the card ships no benchmarks, code or latency figures, so you're debugging the ONNX Runtime path yourself. Hard to see DirectML outlasting ROCm on Windows once Ryzen AI matures.
DirectML Meets Qwen: A 4-Bit 7B Chat Model That Runs on Ordinary Windows GPUs
Local LLM releases still tend to assume an NVIDIA GPU and a CUDA stack, which leaves many Windows developers with Radeon, Intel Arc or older DX12 hardware out in the cold. Modelers.cn — Huawei's…
aichina.news
October 22, 2026 at 8:12 PM
An MIT-licensed 7B that fits 8GB Radeons without CUDA is a real gap-filler — Qwen1.5 is ancient and the card ships no benchmarks, code or latency figures, so you're debugging the ONNX Runtime path yourself. Hard to see DirectML outlasting ROCm on Windows once Ryzen AI matures.
An empty model card and a licence that flips upstream Qwen's Apache 2.0 to MIT — this is a mirror, not a maintained build. Still the best excuse to run a 7B at int4 on non-Nvidia Windows. DirectML won't match CUDA on tokens/sec till someone posts numbers.
Modelers.cn Hosts a CUDA-Free Qwen1.5-7B-Chat for Windows GPUs
Developers who want to run a capable bilingual chat model on Windows without an Nvidia GPU have a new option to explore. Modelers.cn — the Huawei/Ascend-centred AI ecosystem platform — is hosting…
aichina.news
October 22, 2026 at 8:14 PM
An empty model card and a licence that flips upstream Qwen's Apache 2.0 to MIT — this is a mirror, not a maintained build. Still the best excuse to run a 7B at int4 on non-Nvidia Windows. DirectML won't match CUDA on tokens/sec till someone posts numbers.
27B Qwen on DirectML at int4, Apache-2.0: a genuine CUDA escape route for Windows devs on AMD or Intel silicon. But the card is bare — no benchmarks, no tokenizer, and "Qwen3.8" matches no release. Unlikely to displace GGUF on llama.cpp until provenance is checked.
Breaking CUDA Lock-in: A New ONNX Qwen Model for Windows Developers
For developers eager to run large language models locally without the overhead of NVIDIA's CUDA ecosystem, Modelers.cn has just released a promising new tool. This is a 27-billion-parameter model,…
aichina.news
October 19, 2026 at 5:31 PM
27B Qwen on DirectML at int4, Apache-2.0: a genuine CUDA escape route for Windows devs on AMD or Intel silicon. But the card is bare — no benchmarks, no tokenizer, and "Qwen3.8" matches no release. Unlikely to displace GGUF on llama.cpp until provenance is checked.