- Bump vllm[flashinfer] to 0.19.1 in Dockerfile
- Add OpenAIServingRender (new required dependency in 0.19.x serving layer)
- Pass openai_serving_render to all four serving class constructors
- Remove log_error_stack param (removed upstream in 0.19.x)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* VLLM upgrade to 0.12.0 and compatibility fixes
* MAX_NUM_BATCHED_TOKENS fix and CUDA tester
* Sys kill worker instead of marking as failed
* upgrade to vllm 0.12.0
* Update to vllm 0.15.0 and lora fix
* Update for HUB and removal of deprected env variables
* reverted docker-bake changes
* removed leftovers
* Update src/handler.py
Co-authored-by: Dj Isaac <contact@dejaydev.com>
* Update src/utils.py
Co-authored-by: Dj Isaac <contact@dejaydev.com>
* Update src/handler.py
Co-authored-by: Dj Isaac <contact@dejaydev.com>
* Clean up of docs and comments in code
* nit: lowercase p
* nit: lowercase p
---------
Co-authored-by: Dj Isaac <contact@dejaydev.com>
Co-authored-by: chrisvela <chris.vela@runpod.io>
* fix: update CUDA to 12.4.1 for Blackwell GPU support
- Update Dockerfile base image from CUDA 12.1.0 to 12.4.1
- Update ldconfig path to cuda-12.4
- Update FlashInfer installation to use flashinfer-python package
- Add NVIDIA B200 (Blackwell) to supported gpuIds in hub.json
This fixes the "imagePullAsync: failed to get self-hosted image registry auth"
error when deploying on Blackwell GPUs (RTX PRO 6000, B200) by aligning
the Docker image CUDA version with the allowedCudaVersions in hub.json.
Fixes: DR-1118
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* revert: remove NVIDIA B200 from default gpuIds
The gpuIds in hub.json controls default GPU selection for deployments,
not GPU compatibility. The CUDA 12.4 upgrade is sufficient to enable
Blackwell GPU support.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: remove FlashInfer to avoid JIT compilation errors
FlashInfer requires nvcc to JIT-compile CUDA kernels at runtime for
new GPU architectures (like Blackwell SM 10.0). Since we use the CUDA
base image without the toolkit, nvcc is not available.
vLLM will use its built-in fallback sampling methods instead.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
- You no longer need a linux-based machine or NVIDIA GPUs to build the worker.
- Over 3x lighter Docker image size.
- OpenAI Chat Completion output format (optional to use).
- Extremely fast image build time.
- Docker Secrets-protected Hugging Face token support for building the image with a model baked in without exposing your token.
- Support for `n` and `best_of` sampling parameters, which allow you to generate multiple responses from a single prompt.
- New environment variables for various configuration.
- vLLM Version: 0.2.7
