The best local LLMs for the RTX PRO 6000

Qwen3.8 27B (27B) fits comfortably in the RTX PRO 6000's memory at Q8_0, using about 32GB with 8K context and generating ~46 tokens/sec. It is the most capable model in our catalog that runs well on this card. It is also our top pick for coding, reasoning and working with images.

Qwen3.8 27B, released in August 2026, is one of the strongest open models that fits a single 24GB GPU, with image input, a 262K native context, and switchable thinking. It uses the same hybrid layout as the Qwen3.5 and 3.6 27B models, where three of every four layers use linear attention, so long contexts cost far less memory than on older dense models. At Q4_K_M it needs about 18GB with 8K context, leaving room for 64K or more of context on a 24GB card, and 32GB cards can run Q6_K. It supersedes Qwen3.6 27B and Qwen3 32B, but its long reasoning traces make speed on slower hardware a real consideration.

Qwen3.5 122B-A10B (122B (10B active)) fits comfortably in the RTX PRO 6000's memory at Q4_K_M, using about 77GB with 8K context and generating ~105 tokens/sec. It is the next-best model that runs well here, and the one to try if the top pick doesn't suit you. It is also the largest model that runs fully on this GPU.

Qwen3.5 122B-A10B is the mid-size mixture-of-experts model from Qwen's February 2026 Qwen3.5 launch, with 10B active parameters, image input, a 262K context, and switchable thinking. Later Qwen releases refreshed the 27B and 35B-A3B sizes, so this remains Qwen's main model for 96GB to 128GB machines. It needs about 77GB at Q4_K_M with 8K context, which suits Macs with 96GB or more, Strix Halo 128GB systems, and DGX Spark; a 24GB GPU needs 96GB or more of system RAM to hold the offloaded experts. Its hybrid attention keeps the KV cache small, so long contexts are cheap.

gpt-oss-20b (20.9B (3.6B active)) fits comfortably in the RTX PRO 6000's memory at MXFP4, using about 15GB with 8K context and generating ~190 tokens/sec. Pick it when responsiveness matters more than squeezing out maximum quality.

gpt-oss-20b is OpenAI's small open-weight reasoning model, a 21B mixture-of-experts with 3.6B active parameters whose expert weights ship natively in 4-bit MXFP4. OpenAI has not released a successor, and it remains one of the best reasoning and tool-calling models that fits a 16GB GPU. The native weights take about 14GB, so 16GB cards and Macs with 24GB or more run it fully in memory, and 12GB cards run it with a little CPU offload. Reasoning effort is adjustable (low, medium, high) but cannot be turned off.