Can Desktop PC · GeForce RTX 5070 · 32 GB RAM run GLM-4.6V-Flash (10B)?
12 GB VRAM + 32 GB RAM · 672 GB/s memory bandwidth · GLM-4.6V-Flash (10B) has 10.3B parameters.
Short answer
Yes, excellent for chat and acceptable or better for every workload we test.
Best quantization for chat: Q6_K (8.0 GB of weights) on Ollama. Generation: 40–65 tok/s, leaving about 1.8 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.
| Workload | Verdict | Quant | Generation | First reply | Context | |
|---|---|---|---|---|---|---|
| Chatwith Open WebUI | Excellent | Q6_K | 40–65 tok/s | under 1 s | 8K | Details |
| Coding questionswith Continue | Comfortable | Q6_K | 40–65 tok/s | ≈2 s | 16K | Details |
| Coding in a repositorywith Aider | Acceptable | Q5_K_M | 40–70 tok/s | ≈7 s | 32K | Details |
| Agentic codingwith OpenCode | Acceptable | Q5_K_M | 40–65 tok/s | ≈10 s | 32K | Details |
| Long documents (64K)with Open WebUI | Excellent | Q4_K_M | 40–65 tok/s | ≈35 s | 64K | Details |
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
Get it running
Copy-paste commands for the Q4_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.
ollama run hf.co/unsloth/GLM-4.6V-Flash-GGUF:Q4_K_M
Downloads 6.17 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.
Other models for Desktop PC · GeForce RTX 5070 · 32 GB RAM
The best-rated models for chat on this machine.
- MiniCPM5 2B120–200 tok/sExcellent
- Qwen3.5 9B40–65 tok/sExcellent
- Qwen3.5 4B75–120 tok/sExcellent
- Gemma 4 12B40–65 tok/sExcellent
- Qwen3 8B45–75 tok/sExcellent
- DeepSeek-R1-0528-Qwen3-8B45–75 tok/sExcellent
Cheapest machines for GLM-4.6V-Flash (10B)
Hardware rated comfortable or better for chat, lowest price first.
Different tool, context size or workload?
These pages use typical settings. The full check models your exact setup.