Can I Run GLM-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 1 maja 2026
No
No — GLM-5 / GLM-5.1 does not fit in RTX 4060 Laptop (8 GB VRAM, 16 GB RAM). Even at Q2_K it needs about 250.6 GB against 8 GB usable, a shortfall of roughly 242.6 GB.
Confidence: medium
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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model
| Usable memory for models | 8 GB |
| Memory bandwidth | 256 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $1,099 (lib/data/laptops.ts (street price), checked 2026-07-06) |
GLM-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization
| Quant | Memory needed | Fits 8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 1494 GB | ✗ No | — | — | 1488 GB |
| Q8_0 | 796.5 GB | ✗ No | — | — | 790.5 GB |
| Q6_K | 616.1 GB | ✗ No | — | — | 610.1 GB |
| Q5_K_M | 533.3 GB | ✗ No | — | — | 527.3 GB |
| Q4_K_M | 455.2 GB | ✗ No | — | — | 449.2 GB |
| Q3_K_M | 323.2 GB | ✗ No | — | — | 317.1 GB |
| Q2_K | 250.6 GB | ✗ No | — | — | 244.6 GB |
What to watch out for
- 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 4060 laptop limitations
- 8 GB VRAM limits you to 7–8B models at Q4 with a short context.
- System RAM is usually upgradeable on this class of laptop even though VRAM is not.
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.
- 8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
- 16 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-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
No — GLM-5 / GLM-5.1 does not fit in RTX 4060 Laptop (8 GB VRAM, 16 GB RAM). Even at Q2_K it needs about 250.6 GB against 8 GB usable, a shortfall of roughly 242.6 GB.
What limits GLM-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Memory. RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) provides about 8 GB usable, and the smallest quantization of this model needs 250.6 GB.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
- GLM-5 / GLM-5.1 on MacBook Pro M4 Max 128 GB
- GLM-5 / GLM-5.1 on MacBook Pro M4 Max 48 GB
- GLM-5 / GLM-5.1 on MacBook Pro M4 Pro 24 GB
- GLM-5 / GLM-5.1 on MacBook Air M4 16 GB
Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Granite 3.0 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- IBM Granite 4.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- IBM Granite 4.2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- InternLM 3 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- LFM2.5 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
GLM-5 / GLM-5.1 on GPUs
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5090
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5070
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060 Ti 8GB
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060
What This Model Is Good At
- Best local LLMs for coding
- Best local LLMs for document analysis
- Best local LLMs for enterprise assistant
Model & Tools
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