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How to run a local 262K-context model server on a 24 GB card with Llam Ampere

Build llama-server for an RTX 3090 and serve Qwen3.8-27B on a local OpenAI-compatible API.

Authorization notice. Llam Ampere is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA developer with one RTX 3090 or 3090 Ti who wants a private, local model endpoint for coding or agent tools and needs long context.

The problemRunning a 27B model with a very long context on a single consumer card usually needs heavy tuning, and the defaults are often too slow or too large for memory.

The outcomeA llama-server process listening on http://127.0.0.1:8080 with a 262,144-token context, the MTP drafter and the prompt cache on by default.

What you need first

  • Linux machine with an NVIDIA card (RTX 3090 or 3090 Ti with 24 GB for the main path, or a 12 GB card for the low-memory path)
  • CUDA toolkit (the README tested with 12.4), CMake, git and a C++ compiler
  • Disk space and network access for the model download: 15.6 GB for the 24 GB path, 9.0 GB for the 12 GB path
  • Recommended: OpenSSL development files are not needed if you use the -DLLAMA_BUILD_BORINGSSL=ON build flag, which lets -hf download the model

Steps

  1. Run git clone https://github.com/JakeATX/llamAmpere.git and then cd llamAmpere.
  2. Run cmake -S . -B build-sm86 -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=86 -DLLAMA_BUILD_BORINGSSL=ON. (inferred, check the README)
  3. Run cmake --build build-sm86 -j8 --target llama-server.
  4. Run ./build-sm86/bin/llama-server --hf-repo jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF --hf-file ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M.gguf -c 262144 -ngl 99 -fa on -ctk tq5_0 -ctv turbo4 -b 4096 -ub 1024 --parallel 1. (inferred, check the README)
  5. Check that the server is up at http://127.0.0.1:8080 and send one chat request to its OpenAI-compatible API.

Prompt for your agent

In the llamAmpere repo at <repo path>, build llama-server with the SM86 CMake settings and start it with the tq5_0/turbo4 command from the README. Send one chat request to http://127.0.0.1:8080 with the prompt <test prompt>, and report the reply text and the HTTP status. Verify that the server accepted a context size of 262144.

Caveat. The README tests the build on Linux with CUDA 12.4 and a 24 GB NVIDIA card, and the model is an uncensored fine-tune, so review its outputs before use.

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