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DeepSeek-V4-Flash-GGUF

teamblobfish

GGUF quantizations of `deepseek-ai/DeepSeek-V4-Flash` for use with the V4-aware llama.cpp fork at `cchuter/llama.cpp` @ `feat/v4-port-cuda`. > 📦 **Required: V4-aware llama.cpp fork.** These quants don't load on upstream `ggml-org/llama.cpp` — V4 architecture support (compressor decode, hyperconnection, lightning indexer, FP8 KV simulation, NextN heads) lives only in the fork: > > > > Full build + run instructions in Loading below. > 🖥️ **Supported backends: Apple Silicon (Metal), NVIDIA CUDA (Ada/Blackwell), and CPU.** All 5 V4 custom ops (`ggml_dsv4_rope_tail`, `ggml_dsv4_hc_split_sinkhorn`, `ggml_dsv4_hc_weighted_sum`, `ggml_dsv4_hc_expand`, `ggml_dsv4_fp8_kv_quantize`) have Metal kernels AND CUDA kernels in this fork (validated 19/19 on RTX 5090, CUDA 12.8, SM_120 native). The CUDA FP8 path is gated behind `__CUDA_ARCH__ >= 890`; older NVIDIA hardware (Volta/Turing/Ampere) uses a software-emulated FP8 path that builds cleanly under `-DCMAKE_CUDA_ARCHITECTURES=70` but hasn't been runtime-validated yet. **CUDA testers wanted** — file issues at the fork if you hit problems. ROCm / Vulkan / Metal-on-AMD have no V4 kernels and will fail at the first dsv4 op.

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