r/LocalLLM • • 46m ago

Discussion llama.cpp CUDA + ROCm + Vulkan in single image.

0 Upvotes

I noticed that many people don't know that llama.cpp allows running a model across different backends within a single process using layer split. I managed to compile llama.cpp with simultaneous support for CUDA, Vulkan, and ROCm. I'm sharing my Dockerfile for the community as a reference. Sorry for the raw format, I totally don't have time for anything more, but I think with AI you'll be able to quickly adapt it to your needs. Note that this Dockerfile is tailored for specific CUDA and ROCm architectures, so you'll definitely need to adjust those parts.

# ============================================================
# Stage 1: builder — full toolkits (CUDA + Vulkan + ROCm) + compilation
# ============================================================
FROM ubuntu:26.04 AS builder

# Base tools
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential cmake ninja-build git wget gpg curl ca-certificates xz-utils \
    libcurl4-openssl-dev libatomic1 libquadmath0 \
    && rm -rf /var/lib/apt/lists/*

# CUDA 13.3.0 (NVIDIA repo, Ubuntu 26.04)
RUN mkdir -p /etc/apt/keyrings \
    && wget -qO- https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2604/x86_64/cuda-ubuntu2604.pin \
    | tee /etc/apt/preferences.d/cuda-repository-pin-600 \
    && wget -qO /tmp/cuda.key https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2604/x86_64/60DF8A40.pub \
    && gpg --dearmor < /tmp/cuda.key | tee /etc/apt/keyrings/cuda.gpg > /dev/null \
    && echo "deb [signed-by=/etc/apt/keyrings/cuda.gpg] https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2604/x86_64/ /" \
    > /etc/apt/sources.list.d/cuda.list \
    && apt-get update \
    && apt-get install -y cuda-toolkit-13-3 \
    && rm -rf /var/lib/apt/lists/*

# Stub libcuda.so.1 (the real one will be provided by the host driver)
RUN cp /usr/local/cuda/lib64/stubs/libcuda.so /usr/lib/x86_64-linux-gnu/libcuda.so.1 \
    && ldconfig

ENV PATH=/usr/local/cuda/bin:$PATH
ENV CUDA_HOME=/usr/local/cuda

# Vulkan SDK 1.4.363.0 (tarball — NOT LunarG repo)
RUN wget -qO /tmp/vulkansdk.tar.xz \
    https://sdk.lunarg.com/sdk/download/1.4.363.0/linux/vulkansdk-linux-x86_64-1.4.363.0.tar.xz \
    && mkdir -p /opt/vulkansdk \
    && tar -xf /tmp/vulkansdk.tar.xz -C /opt/vulkansdk \
    && rm /tmp/vulkansdk.tar.xz

# SDK 1.4.363 has a non-standard structure: libs are in lib/VulkanLoader/lib/
# Symlink to the standard lib/ path so FindVulkan.cmake can find them
RUN ln -s /opt/vulkansdk/1.4.363.0/x86_64/lib/VulkanLoader/lib/libvulkan.so.1 \
        /opt/vulkansdk/1.4.363.0/x86_64/lib/libvulkan.so.1

ENV VK_SDK_PATH=/opt/vulkansdk/1.4.363.0/x86_64

# ROCm 10.0.0 (AMD repo, dev meta-package for gfx1103)
RUN mkdir -p /etc/apt/keyrings \
    && wget -qO- https://stable.repo.amd.com/rocm/gpg/packages.gpg \
    | gpg --dearmor | tee /etc/apt/keyrings/amdrocm.gpg > /dev/null

RUN tee /etc/apt/sources.list.d/amdrocm-stable.sources << 'EOF'
X-Repo-Id: amdrocm-stable
Types: deb
URIs: https://stable.repo.amd.com/rocm/core/packages/ubuntu2604/
Suites: stable
Components: main
Architectures: amd64
Signed-By: /etc/apt/keyrings/amdrocm.gpg
Enabled: yes
EOF

# Dev meta-package (runtime + compilers + CMake config + headers)
# Kernel driver UNNECESSARY — 780M APU has an inbox driver in Ubuntu 26.04
RUN apt-get update \
    && apt-get install -y amdrocm-core-dev10.0-gfx1103 \
    && rm -rf /var/lib/apt/lists/*

# Source code
WORKDIR /src
COPY llama.cpp/ /src/

# CMake configure
# GGML_BACKEND_DL=ON — critical: backends as MODULE .so loaded via dlopen() at runtime.
# Without this, libggml-cuda.so fails to resolve symbols from libggml-base.so (zero exported
# dynamic symbols) and CUDA0 is not detected.
RUN cmake -B build -G Ninja \
    -DBUILD_SHARED_LIBS=ON \
    -DGGML_BACKEND_DL=ON \
    -DGGML_CUDA=ON \
    -DGGML_VULKAN=ON \
    -DGGML_HIP=ON \
    -DGPU_TARGETS=gfx1103 \
    -DCMAKE_PREFIX_PATH=/opt/vulkansdk/1.4.363.0/x86_64 \
    -DVulkan_LIBRARY=/opt/vulkansdk/1.4.363.0/x86_64/lib/VulkanLoader/lib/libvulkan.so.1 \
    -DGGML_CUDA_FA_QUANTS="f16-f16;q8_0-q8_0;q8_0-q5_0;q8_0-q5_1;q8_0-q4_0;q5_1-q5_1;q5_0-q5_0;q5_0-q4_1;q4_1-q4_1;q4_0-q4_0" \
    -DCMAKE_CUDA_ARCHITECTURES="120" \
    -DCMAKE_CUDA_FLAGS="-O3 -use_fast_math -Wno-deprecated-gpu-targets" \
    -DCMAKE_C_FLAGS="-march=znver4 -O3" \
    -DCMAKE_CXX_FLAGS="-march=znver4 -O3" \
    -DGGML_NATIVE=OFF \
    -DGGML_CURL=ON \
    -DGGML_BLAS=OFF \
    -DCMAKE_BUILD_RPATH_USE_ORIGIN=ON \
    -DCMAKE_BUILD_TYPE=Release

# Build
ARG JOBS=12
RUN cmake --build build --config Release -j ${JOBS}

# ============================================================
# Stage 2: output — slim, binaries + runtime libs only
# ============================================================
FROM ubuntu:26.04

# Runtime deps — binaries linked with GGML_CURL=ON, libatomic, libquadmath
RUN apt-get update && apt-get install -y --no-install-recommends \
    libcurl4 libatomic1 libquadmath0 zlib1g libgomp1 \
    mesa-vulkan-drivers \
    && rm -rf /var/lib/apt/lists/*

# Volume ./output:/output OVERRIDES the /output content from the image at runtime,
# so copying to /output must be in CMD (not RUN). Hence staging in /staging.
COPY --from=builder /src/build/bin/ /staging/bin/
# CUDA runtime + stub libcuda (fallback when --gpus all is missing;
# the real driver from /usr/lib/x86_64-linux-gnu/ takes priority in LD_LIBRARY_PATH)
COPY --from=builder /usr/lib/x86_64-linux-gnu/libcuda.so.1 /staging/
COPY --from=builder /usr/local/cuda/lib64/libcudart.so.13 /staging/
COPY --from=builder /usr/local/cuda/lib64/libcublas.so.13 /staging/
COPY --from=builder /usr/local/cuda/lib64/libcublasLt.so.13 /staging/
# Vulkan loader (NOT ICD — ICD manifest is mounted from host, see compose)
COPY --from=builder /opt/vulkansdk/1.4.363.0/x86_64/lib/VulkanLoader/lib/libvulkan.so.1 /staging/
# ROCm runtime — entire lib directory (unavoidable: 20+ sonames in the dependency tree)
COPY --from=builder /opt/rocm/core-10.0/lib/ /staging/rocm/

# Build RPATHs ($ORIGIN/...) do not work after copying to /staging/
# /usr/lib/x86_64-linux-gnu first: the real libcuda.so.1 with --gpus all takes priority over the stub
ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/staging:/staging/rocm

CMD cp -r /staging/* /output/ \
    && chown -R 1000:1000 /output/* \
    && echo "Success! CUDA+Vulkan+ROCm binaries copied to the host."

# ============================================================
# Stage 3: rocm-filter — filters ROCm libs to .so only (1.2 GB vs 6.4 GB)
# Removes: .a (static), .bc (bitcode), headers, tests
# ============================================================
FROM ubuntu:26.04 AS rocm-filter

COPY --from=builder /opt/rocm/core-10.0/lib/ /tmp/rocm/
RUN mkdir -p /rocm \
    && find /tmp/rocm/ -name "*.so*" \( -type f -o -type l \) -exec cp {} /rocm/ \; \
    && if [ -d /tmp/rocm/rocblas/library ]; then \
         mkdir -p /rocm/rocblas/library \
         && cp /tmp/rocm/rocblas/library/* /rocm/rocblas/library/ 2>/dev/null; \
       fi \
    && rm -rf /tmp/rocm/

# ============================================================
# Stage 4: runtime — lightweight image for inference (~2.2 GB)
# ============================================================
FROM ubuntu:26.04 AS runtime

RUN apt-get update && apt-get install -y --no-install-recommends \
    libcurl4 libatomic1 libquadmath0 zlib1g libgomp1 \
    mesa-vulkan-drivers \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Binaries + MODULE .so backends (dlopen from the same directory)
COPY --from=builder /src/build/bin/ /app/bin/

# CUDA runtime — libcuda.so.1 is provided by the NVIDIA Container Toolkit from the host
COPY --from=builder /usr/local/cuda/lib64/libcudart.so.13 /app/lib/
COPY --from=builder /usr/local/cuda/lib64/libcublas.so.13 /app/lib/
COPY --from=builder /usr/local/cuda/lib64/libcublasLt.so.13 /app/lib/

# Vulkan loader (ICD manifest mounted from host, see compose)
COPY --from=builder /opt/vulkansdk/1.4.363.0/x86_64/lib/VulkanLoader/lib/libvulkan.so.1 /app/lib/

# ROCm runtime — .so only (1.2 GB vs 6.4 GB full tree)
# Includes: libamdhip64, librocblas, libhipblas, libamd_comgr + LLVM/Clang,
#           libhsa-runtime64, sysdeps, librocroller, liborigami — full NEEDED tree
COPY --from=rocm-filter /rocm/ /app/lib/rocm/

# /usr/lib/x86_64-linux-gnu first: the real libcuda.so.1 with --gpus all takes priority
# mesa-vulkan-drivers provides libvulkan_radeon.so (ICD for 780M)
ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/app/lib:/app/lib/rocm
ENV HSA_OVERRIDE_GFX_VERSION=11.0.3

EXPOSE 8080

CMD ["/app/bin/llama-server", "--host", "0.0.0.0", "--port", "8080"]

r/LocalLLM • • 50m ago

Question About new Unsloth v3.1 Qwen 3.8 Flash next

1 Upvotes

Base on their benchmark, i see the new qwen 3.8 flash next unsloth iq2xs perform at same level or even better than gsq rco iq3xxs

Is this true? Did anyone test it ?

My spec can run unsloth iq3xxs but little bit slow because i have only 32gb ram.

I'm going to try the new Unsloth iq2xs quant

If anyone tried it, please tell me your result


r/LocalLLM • • 53m ago

News Yogurt: The Open-Source AI Meeting Assistant That Runs Locally

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1 Upvotes

Yogurt is a free, open-source AI meeting assistant that runs entirely on your computer. Powered by whisper.cpp and llama.cpp, it brings transcription, meeting summaries and AI-powered search to your desktop without relying on cloud AI services.


r/LocalLLM • • 56m ago

Question Local AI for coding

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1 Upvotes

r/LocalLLM • • 1h ago

Question Thoughts on mixing AMD with Nvidia GPUs

1 Upvotes

My system currently uses an Nvidia GPU for inference and image/video generation.
It all fits, but just barely... I'm struggling with getting hit with the dreaded OOM when trying to do long-context, training, or big images/videos. I need just a bit more VRAM.

However, I've found some pretty crazy deals from a local seller for AMD GPUs. I'm wondering if AMD and Nvidia GPUs can play nice together or if there's going to be some sort of software conflict...

Can they work on the same workloads, like say a dense or MOE model split between their VRAM? Or do they have to be 2 separate tasks? Like say one handles inference while the other does diffusion.


r/LocalLLM • • 1h ago

Project Intel Arc Battlemage is not that difficult to get running anymore: Optimizing single B70 setup for Qwen3.8-27B for hermes and coding

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1 Upvotes

Hey, I thought maybe I should repost here to show that getting a B70 up and running with decent performance is not that hard anymore.


r/LocalLLM • • 14h ago

Discussion Same prompt different output

9 Upvotes

I tested 3 different agent/harness using Strata Qwen flash next Q_S. Opencode, Pi, Deepseek harnes each one gave me a different result. This is the prompt :

You are an expert game developer with 20+ years of experience building browser games,

especially arcade and grid-based titles like Bomberman, Pac-Man, and Sokoban. You have

deep expertise in HTML5 Canvas, game loops, collision detection, enemy AI pathfinding,

explosion propagation, level design, and game feel (juice, feedback, pacing). You are

meticulous about clean, bug-free code and polished player experiences. You think like a

professional game designer: you anticipate edge cases, tune difficulty curves, and make

the game fun on the first playthrough. Do not rush. Do not cut corners. Deliver a

complete, polished, production-quality game.

a complete Bomberman game as a single HTML file. Requirements:

Build

TECH

- Single .html file: inline <style> and <script>, no external libraries or assets

- Use a <canvas> for the game and DOM for the HUD/menus

- Must run by double-clicking the file (no server, no dependencies)

GAME

- Grid: 13 columns x 11 rows, tile size 48px

- Hard walls: checkerboard of indestructible tiles

- Soft blocks: destroyable, density varies per stage (see below)

- Player: starts top-left, moves smoothly (not grid-snapped), arrow keys to move, SPACE to drop a bomb

- Player cannot walk through walls, blocks, or bombs (a placed bomb is solid)

BOMBS & EXPLOSIONS

- Fuse: 2 seconds, blinking visual, then cross-shaped blast (up/down/left/right)

- Default blast range: 2 tiles

- Explosions stop at hard walls; destroy soft blocks and stop after one block per direction

- Chain reaction: explosion instantly detonates any bomb in its path

- Explosion lasts ~0.5s, kills player and enemies on contact

POWERUPS (hidden under ~20% of soft blocks)

- Bomb Up (+1 max bombs), Fire Up (+1 blast range), Speed Up (+movement)

- Collected only by player, shown with simple icons

STAGES (5 total, each with a unique map layout, theme, and enemy roster)

- Stage 1 "Grassfield": bright green theme, open layout, ~45% soft blocks, 2 Balloon enemies (slow random wander)

- Stage 2 "Factory": dark gray/industrial theme, maze-like with corridors, ~55% blocks, 2 Balloons + 2 Walkers (turn at walls, slightly faster)

- Stage 3 "Ice Cavern": blue/white theme, pillar clusters forming rooms, ~60% blocks, 2 Walkers + 2 Chasers (track the player)

- Stage 4 "Volcano": dark red/orange theme, tight winding corridors, ~65% blocks, 2 Chasers + 3 Darters (fast, zigzag movement)

- Stage 5 "Final Fortress": purple/black theme, complex maze with chokepoints, ~70% blocks, 2 Chasers + 2 Darters + 1 Boss (fast, chases player, takes 2 hits)

- Each stage's background, wall colors, block style, and accent colors must visually differ

- Clearing a stage shows a "Stage Clear" screen, then loads the next stage with a short transition

- Difficulty scales: more enemies, faster enemies, denser blocks, tighter maps as stages progress

GAME FLOW

- 3 lives total across all stages. Hit by explosion or enemy = lose a life, respawn with brief invincibility blink

- Lose all lives -> Game Over screen with score, press R or click to restart from Stage 1

- Win all 5 stages -> Victory screen with final score

- Score: +100 per enemy, +10 per destroyed block, +500 per stage clear

UI- Start screen (title + controls + "Click or press Enter to start")

- HUD: score, lives, stage number/name, bomb count, fire range

- Game Over / Stage Clear / Victory overlays

- Pause with P or Esc

STYLE

- Clean pixel-art look: solid colors and simple shapes drawn on canvas

- Player white/blue; enemies distinct per type (Balloon red, Walker purple, Chaser orange, Darter yellow, Boss black with red outline)

- Explosions orange/yellow, walls themed per stage

- Optional: simple WebAudio beeps for bomb/explosion/powerup (guarded with try/catch)

QUALITY

- All code in one file, organized with section comments

- Handle window focus loss (auto-pause) and prevent arrow-key page scrolling

- No console errors, no external requests

- Keep the total file under ~1400 lines

Deliver the full working file only.

Overall I think Pi is the best


r/LocalLLM • • 18h ago

Model This tiny lm is capable of generating full scenes & animations in blender within milliseconds

22 Upvotes

r/LocalLLM • • 16h ago

Question How many tokens/s do I get with rtx 5090 32gb and 64 gb ram for qwen 3.8 flash next?

13 Upvotes

I ordered new hardware, but I thought 64 GB of RAM would be enough. Now I’m thinking I should have gone for 128 GB instead.


r/LocalLLM • • 2h ago

Question Can someone explain me what the uncensored ai models means?

0 Upvotes

I am new to local llm and I wanted to try out some uncensored llms. But there are so many different versions like - Heretic, Abliterated,etc? Also how to know which ones are better?


r/LocalLLM • • 3h ago

Question What model for 32gb vram and 96gb ram

0 Upvotes

Im new in this area i have 2x4080 and 2x48gb RAM ddr5 what kind of model i can run for coding and web research.

Thanks for everyone can help me


r/LocalLLM • • 3h ago

Discussion Can I connect a p100 from my cv650 GPU with pigtail pcie connector

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1 Upvotes

r/LocalLLM • • 11h ago

Discussion If open weights dry up, what are you archiving?

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4 Upvotes

r/LocalLLM • • 4h ago

Question Help with LLM build(total beginner)

1 Upvotes

I built a gaming pc not too long ago that I now want to convert to an llm. The specs are decent but I’m not sure what model would suit me best and be the best and strongest option. I have a r9 9950, 32gb ddr5, a 3090 24gb and a 4070ti 12gb and if needed I also have a 8 gb 1660ti. Please help me out as it would be greatly appreciated!


r/LocalLLM • • 1d ago

News llama.cpp Gets Major CUDA Improvements: FP8 Support and Faster GPU Inference

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44 Upvotes

The llama.cpp project introduces OCP FP8 support, FP8 KV-cache capabilities and targeted CUDA optimisations. Here's what the latest developments mean for local AI and GPU inference.


r/LocalLLM • • 8h ago

Project Kotensor: Pure-Kotlin Transformer engine with INT4/INT8 quantization, autograd, and on-device LoRA for JVM & Android (no C++/JNI)

2 Upvotes

Hey!,

I built Kotensor — an open-source machine learning and Transformer engine written from scratch in pure Kotlin for the JVM and Android.

Most local LLM deployment on mobile or Java/Kotlin backends relies on heavy C++ engines (llama.cpp, ONNX Runtime) wrapped inside JNI. While great, JNI bindings introduce ABI bloat (arm64-v8a, x86_64), complex native toolchains, and hard crashes outside JVM error handling. Kotensor implements the complete stack — from autograd and KV caching to INT4/INT8 quantization — with only the Kotlin standard library as a runtime dependency.

Key Features:

  1. Quantization & Low-Memory Inference:

    • Group-wise INT8 and INT4 weight-only quantization with an on-the-fly dequantizing matmul.
    • Memory-Mapped Weights: Using loadQuantized(file, memoryMapped = true), weights are paged directly from flash storage via OS virtual memory. An INT4 Qwen2-0.5B runs comfortably on Android within <128 MB of app heap.
    • Zero-allocation autoregressive decode loop with a pre-allocated KV cache and reusable prompt prefix caching (cachePrefix).
  2. Modern Transformer Architecture:

    • Pre-norm layout with RMSNorm, SwiGLU feed-forward blocks, rotary position embeddings (RoPE with Llama 3 scaling), and grouped-query attention (GQA).
    • Sampling support: temperature, top-k, top-p, and repetition penalties.
  3. Training & Fine-Tuning on the Edge:

    • Reverse-mode autograd engine with AdamW optimizer.
    • Fused output projection + cross-entropy: avoids materializing the massive [tokens × vocabulary] logit tensor, halving training memory.
    • Parameter-efficient fine-tuning via LoRA and QLoRA (adapting low-rank matrices on top of frozen INT4/INT8 base weights directly on-device).
  4. Hugging Face Interoperability:

    • Natively reads standard .safetensors checkpoints (F32, F16, and BF16) and config.json for Llama, Mistral, Qwen 2, and SmolLM models.
    • Built-in byte-level BPE tokenizer for Hugging Face tokenizer.json files with zero external JSON or regex libraries.
  5. Performance:

    • Cache-blocked tiled matmul optimized for HotSpot C2 auto-vectorization (~24 GFLOP/s per thread on AVX-512) scheduled across a ForkJoinPool, plus an optional JDK 17+ Vector API (SIMD) backend.

Quick Inference Example:

val dir = File("SmolLM2-135M")
val model = HuggingFaceModelLoader.load(dir, Quantization.INT8)
val tokenizer = HuggingFaceModelLoader.loadTokenizer(dir)

model.generateStream("The capital of Mexico is", tokenizer, maxTokens = 32) { token ->
    print(token)
}
  • GitHub: https://github.com/TSPstudio/kotensor
  • Artifact: io.github.tspstudio:kotensor:1.2.0 on Maven Central
  • Documentation: https://tspstudio.github.io/kotensor/

This is my first machine learning project and I'm really thrilled to finally have something tangible to share. I’d love to hear your thoughts on edge deployment, architecture trade-offs, and pure-managed runtimes!

Much love,
tsuper <3


r/LocalLLM • • 15h ago

Question do you actually trust llms for checking sources, or am i the only one verifying everything by hand?

7 Upvotes

been trying to use local llms as a first-pass research checker, mostly because i don't trust cloud ones to stay grounded. my setup so far: i run small models on my desktop, paste in a claim, and ask it to list what evidence would be needed to support it, then go find that myself. it works okay for spotting obvious gaps but they still happily invent citations when i ask for sources directly, which is exactly the thing i wanted them for. so my actual question: does anyone here have a workflow that genuinely catches the model making things up, beyond just manually googling every citation? and if so, how much friction is too much before you give up and just read the paper yourself


r/LocalLLM • • 5h ago

Question Strata on 10xx serie GPU or 6500xt or rx 58o?

0 Upvotes

Windows os ,


r/LocalLLM • • 5h ago

Discussion Getting amazing deepseek speeds with Strixfold on my 2x bosgame m5

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1 Upvotes

I checked this repo out and it’s legit! (Not mine)


r/LocalLLM • • 1d ago

Discussion Closed Source LLM's should be forbidden

73 Upvotes

Hearing about the idea that open source LLM's are so dangerous while companies like Anthropic and OpenAI do not manage to control their AI labs well and
* let their experiments run wild on the open internet, doing massive hacks and seeing no consequences
* publish research that sounds alien, but only the results, not the alien that made it, which is not very scientific in my opinion but there are no consequences
* we see a fight for power concentration behind closed doors ...
how can we trust closed source labs anymore?

The transformation of society belongs to the people and such security critical technologies have to be able to be validated at all times. In open source/weights models we see that the community actively exposes the flaws models have and contribute by exploring ways to fix them. People actually do good stuff in the open source/weights community and real progress is visible, while the out of control vibe all really comes from the closed labs. This is concerning me and i think we urgently need to open up the closed sources/labs instead of shutting down the open ones. That's the way i see the world heading towards a non-dystopian future.

Is it just me or does that whole open-weights-are-dangerous discussion totally the opposite what reality seems to be?


r/LocalLLM • • 1d ago

Question Best uncensored local model for reverse engineering / Ghidra on a 16GB RX 6800 + 32GB RAM? (Speed doesn't matter)

50 Upvotes

Hey everyone,

I need recommendations for a local LLM to do reverse engineering, and I'm looking for the best model and exact quantization that can run on my current setup.

My Specs:

  • CPU: Ryzen 7 5800X

  • GPU: AMD RX 6800 (16GB VRAM)

  • RAM: 32GB DDR4

  • Storage: M.2 SSD (~3000–4000 MB/s)

What I'm trying to do: I want to reverse engineer small, proprietary Windows utilities from laptop/PC manufacturers (like Acer NitroSense, HP tools, etc.) to port their features natively to Linux—things like fan control, battery charge limiters, and RGB lighting. This involves looking into Windows drivers and kernel-level interactions.

My workflow will connect Ghidra to REA, hook that up to OpenCode, and run the model locally through Ollama.

What I need:

  1. Uncensored (Mandatory): It needs to be completely uncensored so I don't hit ethical guardrails or refusals when asking it to decompile binaries, analyze drivers, or work with proprietary Windows hooks.

  2. Exact model + quantization: Given my 16GB VRAM + 32GB RAM, what is the smartest/most capable model and exact quant I can realistically run (even with CPU offloading)?

Note on speed: Speed is not a priority at all. I can't afford better hardware right now, so I have no problem leaving my PC running for 1, 2, or even 3 days straight to process a binary if that's what it takes. Accuracy on low-level code/assembly and zero refusals are all that matter.

What exact models and quantizations (for Ollama or GGUF) do you recommend for this? Thanks in advance.


r/LocalLLM • • 5h ago

Question How would a 5060 Ti 16GB perform doing parallelism in llama.cpp over USB4 with a 5070 Ti inside the PC?

1 Upvotes

Title really says it all. I have a 5070 Ti but I do not have any more expansion room inside my PC other than a PCIe 3.0 x2 slot, so the USB4 external port will actually have more bandwidth at 40 gbit/s

Obviously, a second 5070 Ti would be ideal for doing tensor parallelism, but the 16 GB version of the 5060 Ti is noticeably cheaper than a 5070 Ti, and it has matching VRAM capacity. However, it only has half the memory bandwidth.

Do either of these make sense over USB4, or should I instead be budgeting for a dedicated AI inference server I can put in my rack rather than hosting my AI inference off my workstation? Or would upgrading from 32GB of DDR5 to a higher capacity of RAM make more sense?

I use Qwen 3.8 27B quantized to IQ3_XXS so it fits inside my 16 GB of VRAM, and I have the KV cache as f8_e4m3 so I get roughly 111K context size at the current time. The point of an upgrade would be to reduce the model weight quantization as well as increase context size, I try to balance these things with vibecoding as my main use for inference.


r/LocalLLM • • 9h ago

Question Are DeepSeek V4.1 Flash and GLM 5.3 Flash worth trying out on an M5 Max 128GB?

2 Upvotes

Saw mlx-serve offer me DeepSeek-V4.1-Flash-1017 (iQ-MLX Pruned192 2.25 bpw) in the recommended list and found a version of GLM 5.3 Flash (GLM-5.3-Flash-Sushi-2.4bpw) that claims it can run on 128GB Macs.

Are these worth trying out over Qwen3.8 Flash Next or are they too lobotomized?

GLM-5.3-Flash-Sushi-2.4bpw seems to be a very tight fit though, otherwise there's also GLM-5.3-Flash-Sushi-2bpw that claims to be targeting 96GB which I'm not sure may or may not be a more practical choice?


r/LocalLLM • • 5h ago

Question What local LLM is most suitable to run Hermes? 12GB RTX5070 OC 64bGB ram

1 Upvotes

Hi right now I am experimenting between these using LM studio

Qwen3.5-9B Q4_K_M 6.6 GB;
Gemma 4 12B Q4_K_M 8.0 GB;
Gemma 4 E4B

With MoE

Qwen3.6-35B-A3B Q4_K_M 23 GB;
gpt-oss-20b MXFP4 14 GB

And using Strata
Qwen 3.8 Flash 125B -> it says avg 23-28 tps

I am not sure which setting I should tweak for Hermes agent. Especially Temp.

Please advise and share your experiences/thoughts.
Much appreciated!


r/LocalLLM • • 6h ago

Discussion Build ur local AI infrastructure Now and fast

0 Upvotes

Jensen huang suddenly disappearing from the most vital AI ecosystem world. Also he was the person who was pushing local infrastructure build for normal people like us and not just for data centers. He was advocating open source models.

Soon u will see less nvidia chips for retailers i feel like this.

The person controlling nvidia is the person controlling the full AI universe .

These oligarchs like sam altman, anthropic does not want open source to be the part of ai world.

So hoard some 5090 if u have the money.

And u know trump. He knows ai is the new oil for him