Local AI Advisor

Can Desktop PC · RTX PRO 6000 Blackwell · 128 GB RAM run DeepSeek-V4-Flash (284B-A13B)?

96 GB VRAM + 128 GB RAM · 1792 GB/s memory bandwidth · DeepSeek-V4-Flash (284B-A13B) has 284B parameters (13B active per token).

Short answer

It loads, but it's borderline even for chat.

Best quantization for chat: Q4_K_M (162 GB of weights) on Ollama. Generation: 7–10 tok/s, leaving about 0.0 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 WebUIBorderlineQ4_K_M7–10 tok/s≈5 s8KDetails
Coding questionswith ContinueBorderlineQ4_K_M7–9 tok/s≈30 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M7–9 tok/s≈2 min32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M7–9 tok/s≈3 min32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M6–8 tok/s≈9 min64KDetails

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 Q4_K_M build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

This build is split into 5 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.