SnowLLM

A local LLM inference framework, hand-tuned for AMD Ryzen AI Max and compatible with other AMD RDNA 3 / RDNA 3.5 APUs.

For more information, visit snowllm.dev (中文).

SnowLLM is fast with:

• Kernels hand-tuned for the Ryzen AI Max series
• Compute precision chosen for what the hardware runs fastest
• Tuned for low-concurrency inference
• Speculative decoding

Recent News

• 2026-10-29 — MiniCPM-V 4.7 on Ryzen 9 7940HS (RDNA 3): 132K context capacity on a 32 GiB Windows machine. Benchmarks.
• 2026-10-26 — DeepSeek V4.1 Flash support, including embedded DSpark decoding.
• 2026-10-21 — MiniCPM-V 4.7 support with adaptive DSpark: 63.7 tok/s at 8K input on Ryzen AI Max+ 395.
• 2026-10-19 — GLM-5.3-Flash support: 26.8 tok/s at 220K input, 4.3× llama.cpp decode throughput, on Ryzen AI Max+ 395.
• 2026-10-12 — Local Web UI for model management and chat.

Demo

https://github.com/user-attachments/assets/fa191709-bca4-4caa-a5ed-9a0ef290a76b

SnowLLM vs. llama.cpp on a single Ryzen AI Max+ 395, both driven through pi.dev on nginx log triage task, running Qwen3.6 35B A3B with a 2.4x speedup!

_{Qwen3.6-35B-A3B UD-Q4_K_XL GGUF for both, on the same hardware, one engine at a time.}

Performance

Full benchmarks · Methodology and runs.

Requirements

• Linux or Windows 11, an AMD Ryzen APU with RDNA 3 or RDNA 3.5 graphics. WSL2 works; ROCm reaches the GPU there through /dev/dxg, so set it up with AMD's WSL guide first
• ROCm 7.x.
• Python 3.10 – 3.14, and ROCm builds of torch and torchvision — see below.

Install

curl -fsSL https://snowllm.dev/install.sh | sh

The default channel is Stable. For Dev:

curl -fsSL https://snowllm.dev/install.sh | sh -s -- --channel dev

• Checks the machine before downloading anything: GPU, driver access, Python, disk, network
• Installs into ~/.local/share/snowllm, links snowllm into ~/.local/bin
• Re-run the same command to upgrade; use --channel stable to switch back to Stable, or --uninstall to remove

Or do it by hand — torch and torchvision must come from AMD's index:

# https://rocm.docs.amd.com/projects/ai-ecosystem/en/latest/frameworks/pytorch/install.html
pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ \
    "torch[device-gfx1151]==2.12.0+rocm7.14.0" \
    "torchvision==0.27.0+rocm7.14.0"
python -m pip install --upgrade snowllm

For Dev, use python -m pip install --upgrade --pre snowllm instead. See switching channels with pip.

Integrations

snowllm pi points Pi at a local server by writing every model to ~/.pi/agent/models.json.

snowllm pi --port 8000                        # a server on this machine
snowllm pi --host 192.168.1.10 --port 8000    # one elsewhere on the network

--host is where Pi should reach the server, localhost by default; --port is the same 8000 the server listens on.

Web UI

Start the Web UI to download models, load or switch checkpoints, chat, and monitor inference through a local Web UI:

snowllm webui                 # opens the Web UI at http://127.0.0.1:8001
snowllm webui --no-browser    # terminal / headless use

The inference API stays at http://127.0.0.1:8000 across model switches. You can also manage models from another terminal:

snowllm load qwen3.6-35b-a3b-fp8 --max-model-len 32k
snowllm status
snowllm unload

See the Web UI guide for configuration overrides, CLI commands, monitoring, and Web UI details.

Get a model

A recipe is one model at one precision. snowllm pull fetches it:

snowllm recipes
snowllm pull qwen3.8-flash-next-q3-k-xl

• Fetches over several connections at once, and resumes where it stopped
• Checks every file against the sha256 the repository publishes
• A recipe that wants a newer snowllm downloads anyway and says which version it wants

Checkpoints live in ~/models by default. Set models_dir in the JSON configuration or override it with SNOWLLM_MODELS. snowllm <recipe> looks there; snowllm pull --dir PATH selects a different download directory.

Use --bundle to read recipes shipped with SnowLLM instead of fetching the online catalogue:

snowllm recipes --bundle
snowllm pull qwen3.8-flash-next-q3-k-xl --bundle

Run

snowllm qwen3.8-flash-next-q3-k-xl
snowllm ~/models/Qwen3.8-Flash-Next-UD-Q3_K_XL
snowllm qwen3.8-flash-next-q3-k-xl --bundle

Common flags (snowllm --help lists every option):

Roadmap

• More models
• Tensor parallelism at any degree
• NPU/GPU co-working prefill
• Native INT4 quantization

Questions

Ask in Discussions. Hardware reports from the rest of the Ryzen AI Max 300 and 400 series and other RDNA 3 / RDNA 3.5 APUs are especially useful.

License

• SnowLLM: Apache-2.0
• SnowLLM-Kernels: All rights reserved

Changelog / 更新日志

0.3.20 — 2026-10-31

Report startup memory shortages sooner and hide superseded development releases on the website.

• Check weight capacity before KV and workspace planning, report shortages without rebuilding the model to search for a context recommendation, and reuse host mapping measurements. 在规划 KV 和工作区前检查权重容量，内存不足时直接报错，不再重建模型搜索推荐上下文，并复用 host 映射统计。
• Use incremental memory accounting with shared storage deduplication, and avoid real pinned memory allocations during simulated planning. 内存统计改为增量计数并保留共享存储去重，模拟规划期间不再实际申请锁页内存。
• Show the Dev installation channel on the website only when its published version is newer than Stable; older Dev links switch to Stable automatically. 官网仅在已发布的 Dev 版本比 Stable 更新时显示 Dev 安装通道，旧 Dev 链接自动切回 Stable。
• Clarify that the DeepSeek V4.1 Flash VQ + Q4_K DSpark configuration needs 128 GiB of RAM on Linux or 192 GiB on Windows. 明确 DeepSeek V4.1 Flash VQ + Q4_K DSpark 配置在 Linux 上需要 128 GiB 内存，Windows 上需要 192 GiB。

0.3.19 — 2026-10-31

Bring bundled recipes, Stable / Dev installation channels, lower memory use, and fixes for DSpark startup and DeepSeek V4.1 prefix caching to the stable release.

• Bundle recipes with the wheel, add MiniCPM mixed-Q3 DSpark profiles including a 32 GiB Windows profile, and provide a dedicated Qwen3.6 Q4_K_XL MTP recipe. wheel 内置配方，新增 MiniCPM 混合 Q3 DSpark 方案及面向 32 GiB Windows 主机的配置，并提供独立的 Qwen3.6 Q4_K_XL MTP 配方。
• Add Stable / Dev installation and update channels, including channel selection on the website and separate upgrade notifications in the terminal and Web UI. 新增 Stable / Dev 安装与更新通道，官网支持通道选择，终端和 Web UI 分别展示对应的升级提示。
• Reduce loading and inference memory, preallocate runtime buffers, and improve automatic weight placement and host memory accounting. 降低加载与推理内存占用，预分配运行时缓冲，改进自动权重放置及主机内存计量。
• Complete DeepSeek V4.1 prefix caching and fix DSpark startup memory planning and profiling warmup with multiple request slots. 补齐 DeepSeek V4.1 前缀缓存，修复 DSpark 启动内存规划及多请求槽位下的 profiling 预热。
• Improve Windows ROCm startup and model compatibility, configure Pi from the recipe catalogue, and print the equivalent startup command after applying recipe defaults. 改进 Windows ROCm 启动和模型兼容性，支持按配方目录配置 Pi，并在应用配方默认值后打印等价启动命令。
• Require SnowLLM-Kernels 0.3.12 and correct the documented MiniCPM-V 4.7 Q4_K_M minimum RAM to 64 GiB. 依赖 SnowLLM-Kernels 0.3.12，并将 MiniCPM-V 4.7 Q4_K_M 的文档最低内存修正为 64 GiB。

0.3.19.dev4 — 2026-10-31

Fix DSpark startup profiling with multiple requests and improve recipe compatibility validation.

• Match DSpark profiling warmup to the planned single head row when prefill does not request logits, preventing workspace shape errors with multiple request slots. DSpark profiling 预热在无需 logits 的 prefill 中使用规划好的单个 head 行，修复多请求槽位下的工作区形状错误。
• Validate published recipe clients against the recipes supported by each client's version. 按各客户端版本支持的配方验证已发布客户端的兼容性。
• Correct the documented minimum RAM for MiniCPM-V 4.7 Q4_K_M to 64 GiB. 将 MiniCPM-V 4.7 Q4_K_M 的文档最低内存修正为 64 GiB。

0.3.19.dev3 — 2026-10-30

Add Stable / Dev installation channels and update checks, with published PyPI versions shown on the homepage.

• Select Stable or Dev on the homepage, with version numbers from PyPI and installation commands for the selected system and APU. 官网支持选择 Stable / Dev，从 PyPI 获取版本号，并按系统与 APU 生成安装命令。
• Add --channel stable|dev to the installer, including switching back from a development build to the latest stable release. 安装脚本新增 --channel stable|dev，支持从开发版切回最新稳定版。
• Stable builds only report newer stable releases; development builds report newer stable and development releases, with separate notes and upgrade commands in the terminal and Web UI. 稳定版只提示更新的稳定版；开发版同时检查两类更新，终端与 Web UI 分别展示更新说明和升级命令。

0.3.19.dev2 — 2026-10-29

Add bundled recipes and a MiniCPM 32 GiB profile, improve runtime allocation and Windows startup, and fix DeepSeek V4.1 prefix reuse and DSpark startup.

• Bundle recipe definitions with the wheel. Use --bundle or b/RECIPE to select them; the Web UI can switch between snowllm.dev and bundled recipes. wheel 内置配方定义，可通过 --bundle 或 b/RECIPE 使用；Web UI 支持在 snowllm.dev 与内置配方之间切换。
• Add a MiniCPM-V 4.7 mixed-Q3 DSpark profile for 32 GiB Windows hosts with a 132K BF16 context, and publish Radeon 780M benchmark comparisons and setup guidance. 新增面向 32 GiB Windows 主机的 MiniCPM-V 4.7 混合 Q3 DSpark 方案，提供 132K BF16 上下文，并补充 Radeon 780M 对比测试和配置指南。
• Reserve scheduler and sampling buffers at startup, reuse speculative penalty storage, and account for pinned host memory over its storage lifetime. 启动时预分配调度与采样缓冲，复用推测解码的惩罚计算存储，并按存储生命周期核算锁页主机内存。
• Discover Windows Torch ROCm kernel archives before GPU startup while preserving explicit user search paths. Windows 上在 GPU 启动前发现 Torch 的 ROCm 内核归档，并保留用户显式配置的搜索路径。
• Complete DeepSeek V4.1 prefix caching, including Engram history and encoder replay state; enable prefix reuse in both V4.1 recipes, including native DSpark. 补齐 DeepSeek V4.1 前缀缓存中的 Engram 历史和 Encoder 回放状态，在两个 V4.1 方案中启用前缀复用，兼容原生 DSpark。
• Fix V4.1 DSpark startup memory planning and adjust automatic weight placement after adaptive profiling to respect the configured memory budget. 修复 V4.1 DSpark 启动内存规划，并在自适应 profiling 后调整自动权重放置，遵守配置的内存预算。
• Add a dedicated Qwen3.6-35B-A3B Q4_K_XL MTP recipe with an embedded MTP head at depth 2; keep MTP disabled in the plain Q4_K_XL recipe. 新增 Qwen3.6-35B-A3B Q4_K_XL MTP 独立方案，使用内嵌 MTP 预测头和深度 2；普通 Q4_K_XL 方案继续关闭 MTP。
• Print the equivalent startup command after applying recipe defaults and user overrides. 应用 recipe 默认值及用户覆盖参数后，打印等价的完整启动命令。
• Show GPU memory shortfalls in readable units with actionable startup diagnostics. 使用易读单位显示 GPU 内存缺口，并提供可操作的启动诊断。
• Remove the SNOWLLM_HF_TOKEN override; use HF_TOKEN, HUGGING_FACE_HUB_TOKEN or the saved Hugging Face token. 移除 SNOWLLM_HF_TOKEN 覆盖变量，改用 HF_TOKEN、HUGGING_FACE_HUB_TOKEN 或已保存的 Hugging Face token。

0.3.19.dev1 — 2026-10-28

Add MiniCPM mixed-Q3 DSpark, reduce memory use, improve Windows compatibility and simplify Pi setup.

• Add a MiniCPM-V 4.7 mixed-Q3 vision recipe with a Q8 DSpark drafter (intended for machines with 32GB of RAM). 新增 MiniCPM-V 4.7 混合 Q3 视觉方案，搭配 Q8 DSpark 草稿模型（为 32GB 内存的机器准备）。
• Reduce memory use during quantized model loading and inference, including DFlash and DSpark decoding. 降低量化模型加载与推理时的内存占用，包括 DFlash 和 DSpark 解码。
• Improve automatic weight placement to reduce unnecessary host offload. 优化自动权重放置，减少不必要的主机卸载。
• Remove the implicit 90% host memory ceiling when no host budget is configured; automatic host weight offload remains disabled by default. 未配置主机预算时不再施加隐含的 90% 主机内存上限，自动主机权重卸载仍默认关闭。
• Fix Windows compatibility for DeepSeek V4 Flash 0731 and GLM 5.3 Flash. DeepSeek V4.1 Flash 1017 also runs, but prefix caching still needs fixes. 修复 DeepSeek V4 Flash 0731 和 GLM 5.3 Flash 的 Windows 兼容性。DeepSeek V4.1 Flash 1017 也可运行，但前缀缓存仍有待修复。
• Configure Pi with all named models and context limits from the recipe catalogue without requiring a running inference service. Pi 配置直接读取 recipe 目录中的全部具名模型及上下文上限，无需提前启动推理服务。
• Require SnowLLM-Kernels 0.3.11. Existing expert disk caches can no longer be reused. 依赖升级至 SnowLLM-Kernels 0.3.11，旧专家磁盘缓存不再复用。

0.3.18 — 2026-10-28

• Fix Flash-Next DFlash2 startup failures. 修复 Flash-Next DFlash2 启动失败的问题。
• Show a one-line summary in update notifications. 更新提示改为显示一行摘要。

0.3.17 — 2026-10-27

• Fix V4.1 DSpark tap buffers for the full decoder replay window and reuse activation workspace after target sampling. 修复 V4.1 DSpark 回放窗口的 tap 缓冲规划，并在主模型采样后复用激活工作区。
• Set both V4.1 recipes to 8192-token prefill chunks and require 0.3.17 for the DSpark recipe. V4.1 普通版和 DSpark 版均默认使用 8192-token 预填充分块，DSpark 方案要求 0.3.17。
• Tighten the V4.1 host memory ceiling to 75% so automatic placement can use smaller weight groups instead of excessive whole-layer offload. V4.1 主机内存上限收紧到 75%，让自动放置选择更小的权重组，避免整层卸载占用过多主机内存。

0.3.16 — 2026-10-27

• Restore a shared KV pool across concurrent requests instead of multiplying context capacity by the request count. 恢复多个并发请求共享 KV 池的容量计算，不再按并发数成倍预留上下文容量。
• Restore the 8192-token default prefill chunk and set it explicitly in Flash-Next recipes, preserving the existing GPU memory utilization limits. 恢复默认 8192-token 预填充分块，并在 Flash-Next 方案中显式指定，保持原有 GPU 显存使用比例。

0.3.15 — 2026-10-27

• Restore the original recipe catalogue for SnowLLM 0.3.13 and use a separate schema-2 catalogue by default in 0.3.15. 恢复 SnowLLM 0.3.13 的原目录格式，0.3.15 默认使用独立的 schema 2 目录。
• Read legacy recipe memory settings, migrate saved default catalogue URLs, and report version requirements before validating future recipe options. 兼容旧 recipe 显存参数，自动迁移已保存的默认目录地址，并在校验未来版本参数前提示版本要求。
• Keep compatibility for 0.3.13 temporarily; a future release will remove compatibility with 0.3.13 and earlier. 暂时保留 0.3.13 兼容支持；后续版本将移除对 0.3.13 及更早版本的兼容支持。

0.3.14 — 2026-10-26

• Add DeepSeek V4.1 Flash text inference, including optional embedded DSpark decoding. 新增 DeepSeek V4.1 Flash 文本推理，支持可选的内置 DSpark 解码。
• Improve prefill and decode efficiency, and fix output agreement between ordinary and speculative decoding at longer contexts. 改进 Prefill 与 Decode 效率，修复长上下文下普通解码与推测解码的输出一致性。
• Add explicit GPU, host and disk memory limits, automatic weight placement and expert disk caching. 新增 GPU、主机和磁盘用量上限、自动权重放置及专家磁盘缓存。
• Fix multipart GGUF reads, loading-time memory accounting, cache lifetimes and Windows memory diagnostics. 修复分片 GGUF 读取、加载阶段内存计量、缓存生命周期和 Windows 内存诊断。
• Show actionable CLI capacity errors and prominent bilingual Web UI failure messages with settings and log shortcuts. CLI 容量报错提供可操作的参数建议，Web UI 新增醒目的双语错误提示及设置、日志入口。
• Show release notes in update notifications and require SnowLLM-Kernels 0.3.9. 更新通知支持显示发布说明，依赖 SnowLLM-Kernels 0.3.9。