Local AI Advisor

Can Desktop PC · 2× GeForce RTX 3090 (48 GB) · 128 GB RAM run Kolibri-1 (78B-A3.5B)?

48 GB VRAM + 128 GB RAM · 936 GB/s memory bandwidth · Kolibri-1 (78B-A3.5B) has 78.1B parameters (3.46B active per token).

Short answer

Yes, excellent for chat; borderline for coding in a repository.

Best quantization for chat: Q4_K_M (45 GB of weights) on Ollama. Generation: 160–270 tok/s, leaving about 1.5 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 WebUIExcellentQ4_K_M160–270 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ4_K_M160–260 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M140–240 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M140–230 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M110–180 tok/s≈8 s64KDetails

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Different tool, context size or workload?

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