Can I Run GLM-6 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated June 26, 2026
Yes — comfortably
Yes, comfortably — GLM-6 9B at Q8_0 needs about 11.6 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~12.4 GB spare and running at ~59.8 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~59.8 tok/s
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RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
GLM-6 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 20 GB | ✓ Yes | 32K | ~34.8 tok/s | 18 GB |
| Q8_0 | 11.6 GB | ✓ Yes | 64K | ~59.8 tok/s | 9.6 GB |
| Q6_K | 9.4 GB | ✓ Yes | 64K | ~73.4 tok/s | 7.4 GB |
| Q5_K_M | 8.4 GB | ✓ Yes | 64K | ~82 tok/s | 6.4 GB |
| Q4_K_M | 7.4 GB | ✓ Yes | 64K | ~92.2 tok/s | 5.4 GB |
| Q3_K_M | 5.8 GB | ✓ Yes | 64K | ~116.7 tok/s | 3.8 GB |
| Q2_K | 5 GB | ✓ Yes | 64K | ~136.6 tok/s | 3 GB |
Which GLM-6 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| GLM-6 355B-A32B | 219.2 GB | ✗ Too large | — |
| GLM-6 9B | 7.4 GB | ✓ Fits | ~92.2 tok/s |
What to watch out for
- 1 larger variant of GLM-6 does not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current card.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 24 GB of VRAM on the NVIDIA GeForce RTX 3090 at 936 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run GLM-6 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — GLM-6 9B at Q8_0 needs about 11.6 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~12.4 GB spare and running at ~59.8 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of GLM-6 should I use on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 11.6 GB of the 24 GB available, downloads as roughly 9.6 GB, and runs at an estimated 59.8 tokens/sec with up to 64K of context.
What limits GLM-6 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- GPT-OSS on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Granite 3.0 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- IBM Granite 4.0 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- IBM Granite 4.1 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- IBM Granite 4.2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
GLM-6 on GPUs
- GLM-6 on NVIDIA GeForce RTX 5070
- GLM-6 on NVIDIA GeForce RTX 5060 Ti 8GB
- GLM-6 on NVIDIA GeForce RTX 5060
- GLM-6 on NVIDIA GeForce RTX 4070 Ti