AI is happening. The future is updating.Qwen3-Coder-30B-A3B-Instruct-AWQ
AWQ **W4A16** (4-bit weights, group size 128) quantization of `Qwen/Qwen3-Coder-30B-A3B-Instruct`, produced with llm-compressor for serving on a single 24 GB GPU with vLLM. Qwen3-Coder-30B-A3B is a Mixture-of-Experts model (128 experts, 8 active per token) using standard **GQA** attention — so it serves on consumer Ada GPUs with the stock attention backends, no special kernels required. Notes: - At `--max-model-len 65536` on a 24 GB card the KV pool holds ~118K tokens (~1.8x concurrency). The base model's native context is 262K; 64K is the practical cap at 24 GB. - Tool calling works via vLLM's first-class `qwen3_coder` parser. - Non-thinking model — no reasoning parser needed. - Pass `--override-generation-config` if your client doesn't send sampling params, or vLLM will default to `temperature=1.0`, which is far too hot for coding.
AWQ **W4A16** (4-bit weights, group size 128) quantization of `Qwen/Qwen3-Coder-30B-A3B-Instruct`, produced with llm-compressor for serving on a single 24 GB GPU with vLLM. Qwen3-Coder-30B-A3B is a Mixture-of-Experts model (128 experts, 8 active per token) using standard **GQA** attention — so it serves on consumer Ada GPUs with the stock attention backends, no special kernels required. Notes: - At `--max-model-len 65536` on a 24 GB card the KV pool holds ~118K tokens (~1.8x concurrency). The base model's native context is 262K; 64K is the practical cap at 24 GB. - Tool calling works via vLLM's first-class `qwen3_coder` parser. - Non-thinking model — no reasoning parser needed. - Pass `--override-generation-config` if your client doesn't send sampling params, or vLLM will default to `temperature=1.0`, which is far too hot for coding.