Local AI Advisor

Can Desktop PC · RTX PRO 6000 Blackwell · 128 GB RAM run Mistral Small 4 (119B-A6.5B)?

96 GB VRAM + 128 GB RAM · 1792 GB/s memory bandwidth · Mistral Small 4 (119B-A6.5B) has 119.4B parameters (6.5B active per token).

Short answer

Yes, excellent for chat and comfortable or better for every workload we test.

Best quantization for chat: Q5_K_M (80 GB of weights) on Ollama. Generation: 140–190 tok/s, leaving about 14 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIExcellentQ5_K_M140–190 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ5_K_M140–190 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderComfortableQ5_K_M130–180 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeComfortableQ5_K_M130–180 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIExcellentQ5_K_M110–160 tok/s≈10 s64KDetails

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the Q5_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q5_K_XL

This build is split into 3 files, which Ollama can't import from Hugging Face. Use llama.cpp or LM Studio instead.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.